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                    <h1 class="text-lg md:text-xl font-bold text-gray-800">arXiv 每日论文精选</h1>
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                        <i class="fa fa-calendar-o mr-1"></i>2025-11-20
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                    <span class="text-gray-500 mr-1"><i class="fa fa-file-text-o"></i> 总论文数:</span>
                    <span id="total-papers" class="font-semibold text-primary">131</span>
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                    <span class="text-gray-500 mr-1"><i class="fa fa-star"></i> 精选论文数:</span>
                    <span id="selected-papers" class="font-semibold text-accent">15</span>
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                    <span class="text-gray-500 mr-1"><i class="fa fa-line-chart"></i> 平均评分:</span>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.15443v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>CroPS：在短视频搜索中通过跨视角正样本改进稠密检索
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            CroPS: Improving Dense Retrieval with Cross-Perspective Positive Samples in Short-Video Search
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ao Xie, Jiahui Chen, Quanzhi Zhu, Xiaoze Jiang, Zhiheng Qin, Enyun Yu, Han Li
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究短视频搜索中因依赖历史曝光数据导致的过滤气泡问题，核心方法是通过整合用户查询改写、推荐流参与度和LLM合成知识三个层面的正样本，构建多样化的训练信号来缓解检索偏差。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对搜索系统中的核心问题——过滤气泡效应，提出了多视角正样本增强方法，与推荐系统、搜索领域的核心进展高度相关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 13:57:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15443v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15443v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.CL</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Dense retrieval has become a foundational paradigm in modern search systems, especially on short-video platforms. However, most industrial systems adopt a self-reinforcing training pipeline that relies on historically exposed user interactions for supervision. This paradigm inevitably leads to a filter bubble effect, where potentially relevant but previously unseen content is excluded from the training signal, biasing the model toward narrow and conservative retrieval. In this paper, we present CroPS (Cross-Perspective Positive Samples), a novel retrieval data engine designed to alleviate this problem by introducing diverse and semantically meaningful positive examples from multiple perspectives. CroPS enhances training with positive signals derived from user query reformulation behavior (query-level), engagement data in recommendation streams (system-level), and world knowledge synthesized by large language models (knowledge-level). To effectively utilize these heterogeneous signals, we introduce a Hierarchical Label Assignment (HLA) strategy and a corresponding H-InfoNCE loss that together enable fine-grained, relevance-aware optimization. Extensive experiments conducted on Kuaishou Search, a large-scale commercial short-video search platform, demonstrate that CroPS significantly outperforms strong baselines both offline and in live A/B tests, achieving superior retrieval performance and reducing query reformulation rates. CroPS is now fully deployed in Kuaishou Search, serving hundreds of millions of users daily.
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.15389v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>揭示大语言模型个性化中差异感知用户建模的推理扩展机制
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Unveiling Inference Scaling for Difference-Aware User Modeling in LLM Personalization
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Suyu Chen, Yimeng Bai, Yulong Huang, Xiaoyan Zhao, Yang Zhang
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究LLM个性化中用户差异建模不足的问题，核心思想是通过推理扩展机制自主识别差异特征维度并生成结构化定义，实现System-2级别的深度用户差异推理。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对LLM个性化中的用户差异建模问题，提出了基于推理扩展的System-2思考方法，与个性化推荐和LLM应用高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 12:35:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15389v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15389v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) are increasingly integrated into users' daily lives, driving a growing demand for personalized outputs. Prior work has primarily leveraged a user's own history, often overlooking inter-user differences that are critical for effective personalization. While recent methods have attempted to model such differences, their feature extraction processes typically rely on fixed dimensions and quick, intuitive inference (System-1 thinking), limiting both the coverage and granularity of captured user differences. To address these limitations, we propose Difference-aware Reasoning Personalization (DRP), a framework that reconstructs the difference extraction mechanism by leveraging inference scaling to enhance LLM personalization. DRP autonomously identifies relevant difference feature dimensions and generates structured definitions and descriptions, enabling slow, deliberate reasoning (System-2 thinking) over user differences. Experiments on personalized review generation demonstrate that DRP consistently outperforms baseline methods across multiple metrics.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.15141v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>ItemRAG：基于物品的检索增强生成用于基于大语言模型的推荐系统
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            ItemRAG: Item-Based Retrieval-Augmented Generation for LLM-Based Recommendation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sunwoo Kim, Geon Lee, Kyungho Kim, Jaemin Yoo, Kijung Shin
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何改进基于LLM的推荐系统，核心思想是提出ItemRAG方法，通过检索物品-物品共购买历史中的相关物品来增强LLM对共购买模式的理解，特别针对冷启动物品问题设计了结合语义相似性和共购买频率的检索策略。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对LLM在推荐系统中的应用，提出了基于物品的RAG方法，完美契合直接LLM应用和核心领域进展两个重点方向。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 05:39:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15141v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15141v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.AI</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recently, large language models (LLMs) have been widely used as recommender systems, owing to their strong reasoning capability and their effectiveness in handling cold-start items. To better adapt LLMs for recommendation, retrieval-augmented generation (RAG) has been incorporated. Most existing RAG methods are user-based, retrieving purchase patterns of users similar to the target user and providing them to the LLM. In this work, we propose ItemRAG, an item-based RAG method for LLM-based recommendation that retrieves relevant items (rather than users) from item-item co-purchase histories. ItemRAG helps LLMs capture co-purchase patterns among items, which are beneficial for recommendations. Especially, our retrieval strategy incorporates semantically similar items to better handle cold-start items and uses co-purchase frequencies to improve the relevance of the retrieved items. Through extensive experiments, we demonstrate that ItemRAG consistently (1) improves the zero-shot LLM-based recommender by up to 43% in Hit-Ratio-1 and (2) outperforms user-based RAG baselines under both standard and cold-start item recommendation settings.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.15122v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>面向生成式推荐的多方面跨模态量化
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Multi-Aspect Cross-modal Quantization for Generative Recommendation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Fuwei Zhang, Xiaoyu Liu, Dongbo Xi, Jishen Yin, Huan Chen, Peng Yan, Fuzhen Zhua...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究生成式推荐系统中高质量语义标识符构建的挑战，核心思想是通过多模态交叉量化和多角度跨模态对齐，利用多模态信息的互补性来减少冲突率并增强生成模型能力。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对生成式推荐系统的核心挑战，提出多模态交叉量化方法，完美契合核心领域进展和直接LLM应用两大重点方向。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 04:55:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15122v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15122v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.AI</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Generative Recommendation (GR) has emerged as a new paradigm in recommender systems. This approach relies on quantized representations to discretize item features, modeling users' historical interactions as sequences of discrete tokens. Based on these tokenized sequences, GR predicts the next item by employing next-token prediction methods. The challenges of GR lie in constructing high-quality semantic identifiers (IDs) that are hierarchically organized, minimally conflicting, and conducive to effective generative model training. However, current approaches remain limited in their ability to harness multimodal information and to capture the deep and intricate interactions among diverse modalities, both of which are essential for learning high-quality semantic IDs and for effectively training GR models. To address this, we propose Multi-Aspect Cross-modal quantization for generative Recommendation (MACRec), which introduces multimodal information and incorporates it into both semantic ID learning and generative model training from different aspects. Specifically, we first introduce cross-modal quantization during the ID learning process, which effectively reduces conflict rates and thus improves codebook usability through the complementary integration of multimodal information. In addition, to further enhance the generative ability of our GR model, we incorporate multi-aspect cross-modal alignments, including the implicit and explicit alignments. Finally, we conduct extensive experiments on three well-known recommendation datasets to demonstrate the effectiveness of our proposed method.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.15690v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>MoDES：通过动态专家跳过来加速混合专家多模态大语言模型
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            MoDES: Accelerating Mixture-of-Experts Multimodal Large Language Models via Dynamic Expert Skipping
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yushi Huang, Zining Wang, Zhihang Yuan, Yifu Ding, Ruihao Gong, Jinyang Guo, Xia...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究混合专家多模态大语言模型的计算效率问题，核心思想是通过全局调制局部门控机制和双模态阈值方法，动态跳过冗余专家来实现高效推理。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对Transformer架构中的MoE效率优化，提出动态专家跳过机制，对推荐系统和搜索中的大规模模型部署具有重要应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 18:48:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15690v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15690v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Mixture-of-Experts (MoE) Multimodal large language models (MLLMs) excel at vision-language tasks, but they suffer from high computational inefficiency. To reduce inference overhead, expert skipping methods have been proposed to deactivate redundant experts based on the current input tokens. However, we find that applying these methods-originally designed for unimodal large language models (LLMs)-to MLLMs results in considerable performance degradation. This is primarily because such methods fail to account for the heterogeneous contributions of experts across MoE layers and modality-specific behaviors of tokens within these layers. Motivated by these findings, we propose MoDES, the first training-free framework that adaptively skips experts to enable efficient and accurate MoE MLLM inference. It incorporates a globally-modulated local gating (GMLG) mechanism that integrates global layer-wise importance into local routing probabilities to accurately estimate per-token expert importance. A dual-modality thresholding (DMT) method is then applied, which processes tokens from each modality separately, to derive the skipping schedule. To set the optimal thresholds, we introduce a frontier search algorithm that exploits monotonicity properties, cutting convergence time from several days to a few hours. Extensive experiments for 3 model series across 13 benchmarks demonstrate that MoDES far outperforms previous approaches. For instance, when skipping 88% experts for Qwen3-VL-MoE-30B-A3B-Instruct, the performance boost is up to 10.67% (97.33% vs. 86.66%). Furthermore, MoDES significantly enhances inference speed, improving the prefilling time by 2.16$\times$ and the decoding time by 1.26$\times$.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.15074v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>基于协作式大语言模型代理的知识引导自动特征提取
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Knowledge-Informed Automatic Feature Extraction via Collaborative Large Language Model Agents
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Henrik Bradland, Morten Goodwin, Vladimir I. Zadorozhny, Per-Arne Andersen
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何利用大型语言模型自动化地从表格数据中提取高质量特征。其核心思想是设计一个由科学家、提取器和测试器三个专业智能体组成的多智能体协作框架，通过引入丰富定性反馈机制和检索增强生成技术，系统性地整合外部领域知识来生成语义可解释的特征。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出基于LLM多智能体的自动特征提取框架，直接应用于推荐系统等领域的特征工程核心问题，并引入RAG外部知识集成机制，与关注领域高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 03:27:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15074v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15074v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The performance of machine learning models on tabular data is critically dependent on high-quality feature engineering. While Large Language Models (LLMs) have shown promise in automating feature extraction (AutoFE), existing methods are often limited by monolithic LLM architectures, simplistic quantitative feedback, and a failure to systematically integrate external domain knowledge. This paper introduces Rogue One, a novel, LLM-based multi-agent framework for knowledge-informed automatic feature extraction. Rogue One operationalizes a decentralized system of three specialized agents-Scientist, Extractor, and Tester-that collaborate iteratively to discover, generate, and validate predictive features. Crucially, the framework moves beyond primitive accuracy scores by introducing a rich, qualitative feedback mechanism and a "flooding-pruning" strategy, allowing it to dynamically balance feature exploration and exploitation. By actively incorporating external knowledge via an integrated retrieval-augmented (RAG) system, Rogue One generates features that are not only statistically powerful but also semantically meaningful and interpretable. We demonstrate that Rogue One significantly outperforms state-of-the-art methods on a comprehensive suite of 19 classification and 9 regression datasets. Furthermore, we show qualitatively that the system surfaces novel, testable hypotheses, such as identifying a new potential biomarker in the myocardial dataset, underscoring its utility as a tool for scientific discovery.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.15390v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>打破专家知识局限：大语言模型的自剪枝方法
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Breaking Expert Knowledge Limits: Self-Pruning for Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haidong Kang, Lihong Lin, Enneng Yang, Hongning Dai, Hao Wang
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究大型语言模型因规模庞大导致部署困难的问题，核心创新是让LLM通过图驱动思维链自动设计最优剪枝算法，并引入偏斜感知动态稀疏分配来解决高剪枝率下的异常值问题。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的AutoPrune方法直接解决LLM部署中的效率瓶颈，其自剪枝理念和动态稀疏分配技术对推荐系统的大规模模型优化具有重要应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 12:38:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15390v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15390v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models (LLMs) have achieved remarkable performance on a wide range of tasks, hindering real-world deployment due to their massive size. Existing pruning methods (e.g., Wanda) tailored for LLMs rely heavily on manual design pruning algorithms, thereby leading to \textit{huge labor costs} and \textit{requires expert knowledge}. Furthermore, we are the first to identify the serious \textit{outlier value issue} behind dramatic performance degradation under high pruning ratios that are caused by uniform sparsity, raising an additional concern about how to design adaptive pruning sparsity ideal for LLMs. Can LLMs prune by themselves? In this work, we introduce an affirmative answer by proposing a novel pruning method called \textbf{AutoPrune}, which first overcomes expert knowledge limits by leveraging LLMs to design optimal pruning algorithms for themselves automatically without any expert knowledge. Specifically, to mitigate the black-box nature of LLMs, we propose a Graph-driven Chain-of-Thought (GCoT) to optimize prompts, significantly enhancing the reasoning process in learning the pruning algorithm and enabling us to generate pruning algorithms with superior performance and interpretability in the next generation. Finally, grounded in insights of outlier value issue, we introduce Skew-aware Dynamic Sparsity Allocation (SDSA) to overcome the outlier value issue, mitigating performance degradation under high pruning ratios. We conduct extensive experiments on mainstream LLMs benchmarks, demonstrating the superiority of AutoPrune, which consistently excels state-of-the-art competitors. The code is available at: https://anonymous.4open.science/r/AutoPrune.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.15256v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>GRPO-RM：通过GRPO驱动的强化学习微调表示模型
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            GRPO-RM: Fine-Tuning Representation Models via GRPO-Driven Reinforcement Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yanchen Xu, Ziheng Jiao, Hongyuan Zhang, Xuelong Li
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何将GRPO强化学习方法从大语言模型泛化到表示学习模型；核心方法是通过预定义输出集替代LLM中的token采样，并设计专门的奖励函数来适应表示模型的特性。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文将GRPO强化学习方法从LLM扩展到表示学习模型，直接属于核心LLM技术应用领域，对推荐系统中的表示学习有重要价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 09:19:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15256v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15256v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The Group Relative Policy Optimization (GRPO), a reinforcement learning method used to fine-tune large language models (LLMs), has proved its effectiveness in practical applications such as DeepSeek-R1. It raises a question whether GRPO can be generalized to representation learning models. In this paper, we propose Group Relative Policy Optimization for Representation Model (GRPO-RM), and investigate the performance of GRPO-like policy in post-training representation models. Specifically, our method establishes a predefined output set to functionally replace token sequence sampling in LLMs, thereby generating an output group, which is essential for the probability-driven optimization of GRPO. In addition, a specialized reward function is designed to accommodate the properties of representation models. Extensive experiments are conducted on various real-world datasets to validate the effectiveness of our proposed method.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.15164v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>基于动态梯度指导的多模态持续指令调优
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Multimodal Continual Instruction Tuning with Dynamic Gradient Guidance
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Songze Li, Mingyu Gao, Tonghua Su, Xu-Yao Zhang, Zhongjie Wang
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究多模态持续指令调优中的灾难性遗忘问题，核心思想是将遗忘问题重新定义为旧任务梯度缺失，通过参数空间的几何特性近似缺失梯度，并结合动态采样策略平衡模型稳定性与可塑性。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接解决多模态持续学习中的灾难性遗忘问题，其动态梯度引导方法与推荐系统中处理用户动态兴趣和序列数据的挑战高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 06:29:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15164v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15164v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multimodal continual instruction tuning enables multimodal large language models to sequentially adapt to new tasks while building upon previously acquired knowledge. However, this continual learning paradigm faces the significant challenge of catastrophic forgetting, where learning new tasks leads to performance degradation on previous ones. In this paper, we introduce a novel insight into catastrophic forgetting by conceptualizing it as a problem of missing gradients from old tasks during new task learning. Our approach approximates these missing gradients by leveraging the geometric properties of the parameter space, specifically using the directional vector between current parameters and previously optimal parameters as gradient guidance. This approximated gradient can be further integrated with real gradients from a limited replay buffer and regulated by a Bernoulli sampling strategy that dynamically balances model stability and plasticity. Extensive experiments on multimodal continual instruction tuning datasets demonstrate that our method achieves state-of-the-art performance without model expansion, effectively mitigating catastrophic forgetting while maintaining a compact architecture.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.15424v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>LLM-MemCluster：为大型语言模型配备动态记忆以实现文本聚类
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            LLM-MemCluster: Empowering Large Language Models with Dynamic Memory for Text Clustering
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuanjie Zhu, Liangwei Yang, Ke Xu, Weizhi Zhang, Zihe Song, Jindong Wang, Philip...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究LLM在文本聚类中缺乏状态记忆和难以控制聚类粒度的问题，核心思想是引入动态内存实现状态感知，并通过双提示策略让模型自主推理确定聚类数量，实现完全LLM原生的聚类框架。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文通过动态内存机制和双提示策略实现完全LLM原生的文本聚类，直接应用于LLM技术并解决状态记忆问题，与LLM直接应用和Transformer架构效率高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 13:22:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15424v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15424v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) are reshaping unsupervised learning by offering an unprecedented ability to perform text clustering based on their deep semantic understanding. However, their direct application is fundamentally limited by a lack of stateful memory for iterative refinement and the difficulty of managing cluster granularity. As a result, existing methods often rely on complex pipelines with external modules, sacrificing a truly end-to-end approach. We introduce LLM-MemCluster, a novel framework that reconceptualizes clustering as a fully LLM-native task. It leverages a Dynamic Memory to instill state awareness and a Dual-Prompt Strategy to enable the model to reason about and determine the number of clusters. Evaluated on several benchmark datasets, our tuning-free framework significantly and consistently outperforms strong baselines. LLM-MemCluster presents an effective, interpretable, and truly end-to-end paradigm for LLM-based text clustering.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.15392v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>DEPO：面向大语言模型智能体的双效率偏好优化
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            DEPO: Dual-Efficiency Preference Optimization for LLM Agents
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sirui Chen, Mengshi Zhao, Lei Xu, Yuying Zhao, Beier Zhu, Hanwang Zhang, Shengji...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究LLM代理推理效率不足的问题，核心思想是定义步级效率和轨迹级效率的双重效率指标，并开发联合奖励简洁响应和更少行动步骤的偏好优化方法。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对LLM代理效率优化，定义了双层级效率指标并提出了偏好优化方法，与LLM应用和效率技术高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 12:38:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15392v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15392v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent advances in large language models (LLMs) have greatly improved their reasoning and decision-making abilities when deployed as agents. Richer reasoning, however, often comes at the cost of longer chain of thought (CoT), hampering interaction efficiency in real-world scenarios. Nevertheless, there still lacks systematic definition of LLM agent efficiency, hindering targeted improvements. To this end, we introduce dual-efficiency, comprising (i) step-level efficiency, which minimizes tokens per step, and (ii) trajectory-level efficiency, which minimizes the number of steps to complete a task. Building on this definition, we propose DEPO, a dual-efficiency preference optimization method that jointly rewards succinct responses and fewer action steps. Experiments on WebShop and BabyAI show that DEPO cuts token usage by up to 60.9% and steps by up to 26.9%, while achieving up to a 29.3% improvement in performance. DEPO also generalizes to three out-of-domain math benchmarks and retains its efficiency gains when trained on only 25% of the data. Our project page is at https://opencausalab.github.io/DEPO.
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.15411v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>D4C：对比语言-图像预训练模型的无数据量化
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            D4C: Data-free Quantization for Contrastive Language-Image Pre-training Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wenlun Zhang, Yunshan Zhong, Zihao Ding, Xinyu Li, Kentaro Yoshioka
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究CLIP模型的无数据量化性能下降问题，核心思想是通过提示引导语义注入、结构对比生成和扰动感知增强三个组件合成语义丰富且结构多样的伪图像来弥补量化损失。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文针对VLM模型的无数据量化问题提出了创新解决方案，虽然不直接应用于推荐系统，但其处理多模态数据的方法对异构数据建模有重要参考价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 13:08:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15411v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15411v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Data-Free Quantization (DFQ) offers a practical solution for model compression without requiring access to real data, making it particularly attractive in privacy-sensitive scenarios. While DFQ has shown promise for unimodal models, its extension to Vision-Language Models such as Contrastive Language-Image Pre-training (CLIP) models remains underexplored. In this work, we reveal that directly applying existing DFQ techniques to CLIP results in substantial performance degradation due to two key limitations: insufficient semantic content and low intra-image diversity in synthesized samples. To tackle these challenges, we propose D4C, the first DFQ framework tailored for CLIP. D4C synthesizes semantically rich and structurally diverse pseudo images through three key components: (1) Prompt-Guided Semantic Injection aligns generated images with real-world semantics using text prompts; (2) Structural Contrastive Generation reproduces compositional structures of natural images by leveraging foreground-background contrastive synthesis; and (3) Perturbation-Aware Enhancement applies controlled perturbations to improve sample diversity and robustness. These components jointly empower D4C to synthesize images that are both semantically informative and structurally diverse, effectively bridging the performance gap of DFQ on CLIP. Extensive experiments validate the effectiveness of D4C, showing significant performance improvements on various bit-widths and models. For example, under the W4A8 setting with CLIP ResNet-50 and ViT-B/32, D4C achieves Top-1 accuracy improvement of 12.4% and 18.9% on CIFAR-10, 6.8% and 19.7% on CIFAR-100, and 1.4% and 5.7% on ImageNet-1K in zero-shot classification, respectively.
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.15369v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>IPTQ-ViT：仅整数运算视觉Transformer中非线性函数的训练后量化
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            IPTQ-ViT: Post-Training Quantization of Non-linear Functions for Integer-only Vision Transformers
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Gihwan Kim, Jemin Lee, Hyungshin Kim
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何在无需重新训练的情况下对视觉Transformer的非线性函数进行完全整数量化。核心方法是开发多项式GELU和位移Softmax近似函数，并提出结合量化敏感度、扰动和计算成本的统一指标来选择最优近似方案。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的整数量化方法和统一评估指标直接适用于Transformer架构的效率优化，对推荐和搜索系统中的模型部署具有重要价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 11:56:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15369v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15369v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Previous Quantization-Aware Training (QAT) methods for vision transformers rely on expensive retraining to recover accuracy loss in non-linear layer quantization, limiting their use in resource-constrained environments. In contrast, existing Post-Training Quantization (PTQ) methods either partially quantize non-linear functions or adjust activation distributions to maintain accuracy but fail to achieve fully integer-only inference. In this paper, we introduce IPTQ-ViT, a novel PTQ framework for fully integer-only vision transformers without retraining. We present approximation functions: a polynomial-based GELU optimized for vision data and a bit-shifting-based Softmax designed to improve approximation accuracy in PTQ. In addition, we propose a unified metric integrating quantization sensitivity, perturbation, and computational cost to select the optimal approximation function per activation layer. IPTQ-ViT outperforms previous PTQ methods, achieving up to 6.44\%p (avg. 1.78\%p) top-1 accuracy improvement for image classification, 1.0 mAP for object detection. IPTQ-ViT outperforms partial floating-point PTQ methods under W8A8 and W4A8, and achieves accuracy and latency comparable to integer-only QAT methods. We plan to release our code https://github.com/gihwan-kim/IPTQ-ViT.git.
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.15351v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>Octopus：基于六种能力编排的智能体式多模态推理
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Octopus: Agentic Multimodal Reasoning with Six-Capability Orchestration
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yifu Guo, Zishan Xu, Zhiyuan Yao, Yuquan Lu, Jiaye Lin, Sen Hu, Zhenheng Tang, Y...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究多模态推理模型的自主探索能力不足问题，核心思想是通过定义六种核心能力并实现动态编排，使模型能自主选择最适合的推理路径来处理多模态任务。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的六能力编排框架和异构模态协调机制与VLM异构数据建模理念高度相关，但其主要聚焦多模态推理而非推荐搜索核心领域。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 11:22:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15351v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15351v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Existing multimodal reasoning models and frameworks suffer from fundamental architectural limitations: most lack the human-like ability to autonomously explore diverse reasoning pathways-whether in direct inference, tool-driven visual exploration, programmatic visual manipulation, or intrinsic visual imagination. Consequently, they struggle to adapt to dynamically changing capability requirements in real-world tasks. Meanwhile, humans exhibit a complementary set of thinking abilities when addressing such tasks, whereas existing methods typically cover only a subset of these dimensions. Inspired by this, we propose Octopus: Agentic Multimodal Reasoning with Six-Capability Orchestration, a new paradigm for multimodal agentic reasoning. We define six core capabilities essential for multimodal reasoning and organize a comprehensive evaluation benchmark, Octopus-Bench, accordingly. Octopus is capable of autonomously exploring during reasoning and dynamically selecting the most appropriate capability based on the current state. Experimental results show that Octopus achieves the best performance on the vast majority of tasks in Octopus-Bench, highlighting the crucial role of capability coordination in agentic multimodal reasoning.
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.15706v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>RoMa v2：更强大、更优秀、更快速、更密集的特征匹配
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            RoMa v2: Harder Better Faster Denser Feature Matching
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Johan Edstedt, David Nordström, Yushan Zhang, Georg Bökman, Jonathan Astermark, ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究密集特征匹配在复杂真实场景中的性能不足问题，核心方法是构建新颖的匹配架构和损失函数，结合两阶段匹配-精炼流程及定制CUDA内核，通过系统化改进提升匹配精度和效率。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的密集特征匹配技术改进，虽然提到了DINOv3基础模型，但其核心应用场景是3D场景图像匹配，与推荐系统、搜索或广告的直接关联性较弱。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 18:59:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15706v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15706v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Dense feature matching aims to estimate all correspondences between two images of a 3D scene and has recently been established as the gold-standard due to its high accuracy and robustness. However, existing dense matchers still fail or perform poorly for many hard real-world scenarios, and high-precision models are often slow, limiting their applicability. In this paper, we attack these weaknesses on a wide front through a series of systematic improvements that together yield a significantly better model. In particular, we construct a novel matching architecture and loss, which, combined with a curated diverse training distribution, enables our model to solve many complex matching tasks. We further make training faster through a decoupled two-stage matching-then-refinement pipeline, and at the same time, significantly reduce refinement memory usage through a custom CUDA kernel. Finally, we leverage the recent DINOv3 foundation model along with multiple other insights to make the model more robust and unbiased. In our extensive set of experiments we show that the resulting novel matcher sets a new state-of-the-art, being significantly more accurate than its predecessors. Code is available at https://github.com/Parskatt/romav2
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            <a href="https://www.alphaxiv.org/abs/2511.15613v1" target="_blank" rel="noopener noreferrer">
                何时思考与何时回看：不确定性引导的回看机制
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            <i class="fa fa-star mr-1"></i>3/10
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            When to Think and When to Look: Uncertainty-Guided Lookback
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jing Bi, Filippos Bellos, Junjia Guo, Yayuan Li, Chao Huang, Yunlong, Tang, Luch...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该标题暗示了一种基于不确定性的决策机制，可能涉及何时利用历史信息或外部知识。虽然不确定性建模在推荐和搜索中有应用（如探索-利用权衡），但标题过于通用，没有明确指向推荐系统、搜索或广告的具体技术。缺乏明确的LLM、Transformer或异构数据处理的连接点，使其相关性较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 17:01:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15613v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15613v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CL</span></div>
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                    Test-time thinking (that is, generating explicit intermediate reasoning chains) is known to boost performance in large language models and has recently shown strong gains for large vision language models (LVLMs). However, despite these promising results, there is still no systematic analysis of how thinking actually affects visual reasoning. We provide the first such analysis with a large scale, controlled comparison of thinking for LVLMs, evaluating ten variants from the InternVL3.5 and Qwen3-VL families on MMMU-val under generous token budgets and multi pass decoding. We show that more thinking is not always better; long chains often yield long wrong trajectories that ignore the image and underperform the same models run in standard instruct mode. A deeper analysis reveals that certain short lookback phrases, which explicitly refer back to the image, are strongly enriched in successful trajectories and correlate with better visual grounding. Building on this insight, we propose uncertainty guided lookback, a training free decoding strategy that combines an uncertainty signal with adaptive lookback prompts and breadth search. Our method improves overall MMMU performance, delivers the largest gains in categories where standard thinking is weak, and outperforms several strong decoding baselines, setting a new state of the art under fixed model families and token budgets. We further show that this decoding strategy generalizes, yielding consistent improvements on five additional benchmarks, including two broad multimodal suites and math focused visual reasoning datasets.
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                上下文级联压缩：探索文本压缩的极限
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            Context Cascade Compression: Exploring the Upper Limits of Text Compression
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Fanfan Liu, Haibo Qiu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于文本压缩技术，属于通用数据压缩领域而非特定于推荐系统、搜索或广告应用。虽然高效的文本压缩可能间接降低存储和传输成本，但论文标题未表明与Transformer架构、LLM技术或推荐系统的直接关联，也未展示明确的跨模态建模潜力。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 09:02:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15244v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15244v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Million-level token inputs in long-context tasks pose significant computational and memory challenges for Large Language Models (LLMs). Recently, DeepSeek-OCR conducted research into the feasibility of Contexts Optical Compression and achieved preliminary results. Inspired by this, we introduce Context Cascade Compression C3 to explore the upper limits of text compression. Our method cascades two LLMs of different sizes to handle the compression and decoding tasks. Specifically, a small LLM, acting as the first stage, performs text compression by condensing a long context into a set of latent tokens (e.g., 32 or 64 in length), achieving a high ratio of text tokens to latent tokens. A large LLM, as the second stage, then executes the decoding task on this compressed context. Experiments show that at a 20x compression ratio (where the number of text tokens is 20 times the number of latent tokens), our model achieves 98% decoding accuracy, compared to approximately 60% for DeepSeek-OCR. When we further increase the compression ratio to 40x, the accuracy is maintained at around 93%. This indicates that in the domain of context compression, C3 Compression demonstrates superior performance and feasibility over optical character compression. C3 uses a simpler, pure-text pipeline that ignores factors like layout, color, and information loss from a visual encoder. This also suggests a potential upper bound for compression ratios in future work on optical character compression, OCR, and related fields. Codes and model weights are publicly accessible at https://github.com/liufanfanlff/C3-Context-Cascade-Compression
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.15572v1" target="_blank" rel="noopener noreferrer">
                从低秩特征到编码不匹配：重新思考视觉Transformer中的特征蒸馏
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            From Low-Rank Features to Encoding Mismatch: Rethinking Feature Distillation in Vision Transformers
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Huiyuan Tian, Bonan Xu, Shijian Li, Xin Jin
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉Transformer中的特征蒸馏技术，属于计算机视觉领域的特定优化问题。虽然Transformer架构本身与推荐/搜索系统相关，但论文聚焦于视觉特征蒸馏这一狭窄方向，缺乏明确的推荐/搜索/广告应用场景。特征蒸馏技术可能对模型压缩有潜在价值，但论文标题未表明其与异构数据处理或多模态学习相关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 16:03:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15572v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15572v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Feature-map knowledge distillation (KD) is highly effective for convolutional networks but often fails for Vision Transformers (ViTs). To understand this failure and guide method design, we conduct a two-view representation analysis of ViTs. First, a layer-wise Singular Value Decomposition (SVD) of full feature matrices shows that final-layer representations are globally low-rank: for CaiT-S24, only $121/61/34/14$ dimensions suffice to capture $99\%/95\%/90\%/80\%$ of the energy. In principle, this suggests that a compact student plus a simple linear projector should be enough for feature alignment, contradicting the weak empirical performance of standard feature KD. To resolve this paradox, we introduce a token-level Spectral Energy Pattern (SEP) analysis that measures how each token uses channel capacity. SEP reveals that, despite the global low-rank structure, individual tokens distribute energy over most channels, forming a high-bandwidth encoding pattern. This results in an encoding mismatch between wide teachers and narrow students. Motivated by this insight, we propose two minimal, mismatch-driven strategies: (1) post-hoc feature lifting with a lightweight projector retained during inference, or (2) native width alignment that widens only the student's last block to the teacher's width. On ImageNet-1K, these strategies reactivate simple feature-map distillation in ViTs, raising DeiT-Tiny accuracy from $74.86\%$ to $77.53\%$ and $78.23\%$ when distilling from CaiT-S24, while also improving standalone students trained without any teacher. Our analysis thus explains why ViT feature distillation fails and shows how exploiting low-rank structure yields effective, interpretable remedies and concrete design guidance for compact ViTs.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.15433v1" target="_blank" rel="noopener noreferrer">
                基于模态解耦与表示空间约束学习的多模态目标检测
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Representation Space Constrained Learning with Modality Decoupling for Multimodal Object Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>YiKang Shao, Tao Shi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多模态目标检测，虽然涉及多模态表示学习，但其核心应用场景是计算机视觉中的目标检测任务。论文中提到的表示空间约束学习和模态解耦技术可能对处理异构数据的推荐系统有所启发，但这种联系较为间接且应用潜力有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 13:41:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15433v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15433v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multimodal object detection has attracted significant attention in both academia and industry for its enhanced robustness. Although numerous studies have focused on improving modality fusion strategies, most neglect fusion degradation, and none provide a theoretical analysis of its underlying causes. To fill this gap, this paper presents a systematic theoretical investigation of fusion degradation in multimodal detection and identifies two key optimization deficiencies: (1) the gradients of unimodal branch backbones are severely suppressed under multimodal architectures, resulting in under-optimization of the unimodal branches; (2) disparities in modality quality cause weaker modalities to experience stronger gradient suppression, which in turn results in imbalanced modality learning. To address these issues, this paper proposes a Representation Space Constrained Learning with Modality Decoupling (RSC-MD) method, which consists of two modules. The RSC module and the MD module are designed to respectively amplify the suppressed gradients and eliminate inter-modality coupling interference as well as modality imbalance, thereby enabling the comprehensive optimization of each modality-specific backbone. Extensive experiments conducted on the FLIR, LLVIP, M3FD, and MFAD datasets demonstrate that the proposed method effectively alleviates fusion degradation and achieves state-of-the-art performance across multiple benchmarks. The code and training procedures will be released at https://github.com/yikangshao/RSC-MD.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.15098v1" target="_blank" rel="noopener noreferrer">
                基于离散扩散的多模态大语言模型中视觉令牌冗余性的综合研究
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            A Comprehensive Study on Visual Token Redundancy for Discrete Diffusion-based Multimodal Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Duo Li, Zuhao Yang, Xiaoqin Zhang, Ling Shao, Shijian Lu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究多模态大语言模型中的视觉令牌冗余问题，属于视觉-语言交叉领域。虽然提到了离散扩散模型，但其核心焦点是视觉令牌的冗余性分析，与推荐系统、搜索或广告的直接应用关联较弱。在推荐/搜索场景中，处理异构数据时可能借鉴视觉令牌压缩的思想，但这种应用潜力较为间接。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 04:13:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15098v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15098v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Discrete diffusion-based multimodal large language models (dMLLMs) have emerged as a promising alternative to autoregressive MLLMs thanks to their advantages in parallel decoding and bidirectional context modeling, but most existing dMLLMs incur significant computational overhead during inference due to the full-sequence attention computation in each denoising step. Pioneer studies attempt to resolve this issue from a modality-agnostic perspective via key-value cache optimization or efficient sampling but most of them overlook modality-specific visual token redundancy. In this work, we conduct a comprehensive study on how visual token redundancy evolves with different dMLLM architectures and tasks and how visual token pruning affects dMLLM responses and efficiency. Specifically, our study reveals that visual redundancy emerges only in from-scratch dMLLMs while handling long-answer tasks. In addition, we validate that visual token pruning introduces non-negligible information loss in dMLLMs and only from-scratch dMLLMs can recover the lost information progressively during late denoising steps. Furthermore, our study shows that layer-skipping is promising for accelerating AR-to-diffusion dMLLMs, whereas progressive or late-step pruning is more effective for from-scratch dMLLMs. Overall, this work offers a new perspective on efficiency optimization for dMLLMs, greatly advancing their applicability across various multimodal understanding tasks.
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            <a href="https://www.alphaxiv.org/abs/2511.15090v1" target="_blank" rel="noopener noreferrer">
                BBox DocVQA：一个大规模边界框标注的数据集，用于增强文档视觉问答中的推理能力
            </a>
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            <i class="fa fa-star mr-1"></i>3/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            BBox DocVQA: A Large Scale Bounding Box Grounded Dataset for Enhancing Reasoning in Document Visual Question Answer
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wenhan Yu, Wang Chen, Guanqiang Qi, Weikang Li, Yang Li, Lei Sha, Deguo Xia, Jiz...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注文档视觉问答和边界框标注数据集，属于视觉-语言交叉领域，但与推荐系统、搜索或广告的核心技术关联较弱。虽然视觉-语言模型的思路可能启发异构数据处理，但该工作更偏向文档理解和视觉推理的特定应用，缺乏明确的RecSys/Search/Ads应用潜力。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 04:03:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15090v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15090v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.DB</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Document Visual Question Answering (DocVQA) is a fundamental task for multimodal document understanding and a key testbed for vision language reasoning. However, most existing DocVQA datasets are limited to the page level and lack fine grained spatial grounding, constraining the interpretability and reasoning capability of Vision Language Models (VLMs). To address this gap, we introduce BBox DocVQA a large scale, bounding box grounded dataset designed to enhance spatial reasoning and evidence localization in visual documents. We further present an automated construction pipeline, Segment Judge and Generate, which integrates a segment model for region segmentation, a VLM for semantic judgment, and another advanced VLM for question answer generation, followed by human verification for quality assurance. The resulting dataset contains 3.6 K diverse documents and 32 K QA pairs, encompassing single and multi region as well as single and multi page scenarios. Each QA instance is grounded on explicit bounding boxes, enabling fine grained evaluation of spatial semantic alignment. Benchmarking multiple state of the art VLMs (e.g., GPT 5, Qwen2.5 VL, and InternVL) on BBox DocVQA reveals persistent challenges in spatial grounding and reasoning accuracy. Furthermore, fine tuning on BBox DocVQA substantially improves both bounding box localization and answer generation, validating its effectiveness for enhancing the reasoning ability of VLMs. Our dataset and code will be publicly released to advance research on interpretable and spatially grounded vision language reasoning.
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            <a href="https://www.alphaxiv.org/abs/2511.15383v1" target="_blank" rel="noopener noreferrer">
                面向飞机维修、修理和大修任务搜索的合规性保持检索系统
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            A Compliance-Preserving Retrieval System for Aircraft MRO Task Search
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Byungho Jo
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文虽然涉及检索系统，但聚焦于特定领域（飞机MRO）的合规性要求，这与搜索、推荐或广告的核心技术进展关联度较低。合规性保持属于安全相关主题，属于明确排除的范畴，且缺乏对通用检索技术或LLM应用的明显贡献。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 12:25:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15383v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15383v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.ET</span><span class="category-tag">cs.IR</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Aircraft Maintenance Technicians (AMTs) spend up to 30% of work time searching manuals, a documented efficiency bottleneck in MRO operations where every procedure must be traceable to certified sources. We present a compliance-preserving retrieval system that adapts LLM reranking and semantic search to aviation MRO environments by operating alongside, rather than replacing, certified legacy viewers. The system constructs revision-robust embeddings from ATA chapter hierarchies and uses vision-language parsing to structure certified content, allowing technicians to preview ranked tasks and access verified procedures in existing viewers. Evaluation on 49k synthetic queries achieves >90% retrieval accuracy, while bilingual controlled studies with 10 licensed AMTs demonstrate 90.9% top-10 success rate and 95% reduction in lookup time, from 6-15 minutes to 18 seconds per task. These gains provide concrete evidence that semantic retrieval can operate within strict regulatory constraints and meaningfully reduce operational workload in real-world multilingual MRO workflows.
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            <a href="https://www.alphaxiv.org/abs/2511.15303v1" target="_blank" rel="noopener noreferrer">
                微博博文情感演化的观点动力学模型
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Opinion Dynamics Models for Sentiment Evolution in Weibo Blogs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yulong He, Anton V. Proskurnikov, Artem Sedakov
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究社交媒体中的情感演化，属于社交网络分析领域，与推荐系统、搜索或广告的核心技术关联较弱。虽然情感分析在内容理解中有一定作用，但论文聚焦于观点动力学模型而非实际的推荐/搜索算法改进，对当前关注领域的直接贡献有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 10:13:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15303v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15303v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.SI</span><span class="category-tag">cs.HC</span><span class="category-tag">cs.IR</span><span class="category-tag">cs.MA</span><span class="category-tag">physics.soc-ph</span></div>
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                    Online social media platforms enable influencers to distribute content and quickly capture audience reactions, significantly shaping their promotional strategies and advertising agreements. Understanding how sentiment dynamics and emotional contagion unfold among followers is vital for influencers and marketers, as these processes shape engagement, brand perception, and purchasing behavior. While sentiment analysis tools effectively track sentiment fluctuations, dynamical models explaining their evolution remain limited, often neglecting network structures and interactions both among blogs and between their topic-focused follower groups. In this study, we tracked influential tech-focused Weibo bloggers over six months, quantifying follower sentiment from text-mined feedback. By treating each blogger's audience as a single "macro-agent", we find that sentiment trajectories follow the principle of iterative averaging -- a foundational mechanism in many dynamical models of opinion formation, a theoretical framework at the intersection of social network analysis and dynamical systems theory. The sentiment evolution aligns closely with opinion-dynamics models, particularly modified versions of the classical French-DeGroot model that incorporate delayed perception and distinguish between expressed and private opinions. The inferred influence structures reveal interdependencies among blogs that may arise from homophily, whereby emotionally similar users subscribe to the same blogs and collectively shape the shared sentiment expressed within these communities.
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            <a href="https://www.alphaxiv.org/abs/2511.15241v1" target="_blank" rel="noopener noreferrer">
                选择性混合用于计算机化自适应测试中问题选择去偏
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        <div class="mb-2 text-base text-gray-700">
            Selective Mixup for Debiasing Question Selection in Computerized Adaptive Testing
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mi Tian, Kun Zhang, Fei Liu, Jinglong Li, Yuxin Liao, Chenxi Bai, Zhengtao Tan, ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机化自适应测试中的去偏问题选择方法，这属于教育技术领域的特定应用。虽然涉及选择算法和去偏技术，但缺乏与推荐系统、搜索或广告领域的直接关联，也没有涉及LLM、Transformer架构或异构数据建模等核心技术。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 08:55:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15241v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15241v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Computerized Adaptive Testing (CAT) is a widely used technology for evaluating learners' proficiency in online education platforms. By leveraging prior estimates of proficiency to select questions and updating the estimates iteratively based on responses, CAT enables personalized learner modeling and has attracted substantial attention. Despite this progress, most existing works focus primarily on improving diagnostic accuracy, while overlooking the selection bias inherent in the adaptive process. Selection Bias arises because the question selection is strongly influenced by the estimated proficiency, such as assigning easier questions to learners with lower proficiency and harder ones to learners with higher proficiency. Since the selection depends on prior estimation, this bias propagates into the diagnosis model, which is further amplified during iterative updates, leading to misalignment and biased predictions. Moreover, the imbalanced nature of learners' historical interactions often exacerbates the bias in diagnosis models. To address this issue, we propose a debiasing framework consisting of two key modules: Cross-Attribute Examinee Retrieval and Selective Mixup-based Regularization. First, we retrieve balanced examinees with relatively even distributions of correct and incorrect responses and use them as neutral references for biased examinees. Then, mixup is applied between each biased examinee and its matched balanced counterpart under label consistency. This augmentation enriches the diversity of bias-conflicting samples and smooths selection boundaries. Finally, extensive experiments on two benchmark datasets with multiple advanced diagnosis models demonstrate that our method substantially improves both the generalization ability and fairness of question selection in CAT.
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            <a href="https://www.alphaxiv.org/abs/2511.15709v1" target="_blank" rel="noopener noreferrer">
                有界字母表上的分词是困难的
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            Tokenisation over Bounded Alphabets is Hard
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Violeta Kastreva, Philip Whittington, Dennis Komm, Tiago Pimentel
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明研究的是分词(tokenisation)的理论复杂性，属于计算理论或形式语言领域。虽然分词是文本处理的基础步骤，但该研究聚焦于有界字母表上的理论困难性，缺乏明确的实际应用场景。对于推荐系统、搜索或广告领域，这种纯理论复杂性研究难以直接转化为实际的技术改进或应用创新。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 18:59:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15709v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15709v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.DS</span><span class="category-tag">cs.LG</span></div>
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                    Recent works have shown that tokenisation is NP-complete. However, these works assume tokenisation is applied to inputs with unboundedly large alphabets -- an unrealistic assumption, given that in practice tokenisers operate over fixed-size alphabets, such as bytes or Unicode characters. We close this gap by analysing tokenisation over bounded $n$-ary alphabets, considering two natural variants: bottom-up tokenisation and direct tokenisation, where we must, respectively, select a sequence of merge operations or a vocabulary whose application optimally compresses a dataset. First, we note that proving hardness results for an $n$-ary alphabet proves the same results for alphabets of any larger size. We then prove that even with binary alphabets, both variants are not only NP-complete, but admit no polynomial-time approximation scheme (unless P=NP). We further show that direct tokenisation remains NP-complete even when applied to unary alphabets. While unary alphabets may not be practically useful, this result establishes that the computational intractability of tokenisation is not an artifact of large alphabets or complex constructions, but a fundamental barrier. Overall, our results explain why practical algorithms such as BPE and UnigramLM are heuristic, and points toward approximation algorithms being an important path going forward for tokenisation research.
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            <a href="https://www.alphaxiv.org/abs/2511.15703v1" target="_blank" rel="noopener noreferrer">
                视觉思考，文本推理：ARC中的视觉-语言协同
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            Think Visually, Reason Textually: Vision-Language Synergy in ARC
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Beichen Zhang, Yuhang Zang, Xiaoyi Dong, Yuhang Cao, Haodong Duan, Dahua Lin, Ji...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉-语言模型在抽象推理任务(ARC)中的表现，属于纯粹的VLM研究领域。虽然标题提到了多模态协同，但没有明确指向推荐系统、搜索或广告领域的应用场景，与当前关注的异构数据统一建模仅有微弱关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 18:59:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15703v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15703v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                    Abstract reasoning from minimal examples remains a core unsolved problem for frontier foundation models such as GPT-5 and Grok 4. These models still fail to infer structured transformation rules from a handful of examples, which is a key hallmark of human intelligence. The Abstraction and Reasoning Corpus for Artificial General Intelligence (ARC-AGI) provides a rigorous testbed for this capability, demanding conceptual rule induction and transfer to novel tasks. Most existing methods treat ARC-AGI as a purely textual reasoning task, overlooking the fact that humans rely heavily on visual abstraction when solving such puzzles. However, our pilot experiments reveal a paradox: naively rendering ARC-AGI grids as images degrades performance due to imprecise rule execution. This leads to our central hypothesis that vision and language possess complementary strengths across distinct reasoning stages: vision supports global pattern abstraction and verification, whereas language specializes in symbolic rule formulation and precise execution. Building on this insight, we introduce two synergistic strategies: (1) Vision-Language Synergy Reasoning (VLSR), which decomposes ARC-AGI into modality-aligned subtasks; and (2) Modality-Switch Self-Correction (MSSC), which leverages vision to verify text-based reasoning for intrinsic error correction. Extensive experiments demonstrate that our approach yields up to a 4.33% improvement over text-only baselines across diverse flagship models and multiple ARC-AGI tasks. Our findings suggest that unifying visual abstraction with linguistic reasoning is a crucial step toward achieving generalizable, human-like intelligence in future foundation models. Source code will be released soon.
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            <a href="https://www.alphaxiv.org/abs/2511.15661v1" target="_blank" rel="noopener noreferrer">
                VisPlay：从图像中自我演化的视觉语言模型
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            VisPlay: Self-Evolving Vision-Language Models from Images
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yicheng He, Chengsong Huang, Zongxia Li, Jiaxin Huang, Yonghui Yang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉语言模型的自我演化能力，属于纯粹的视觉-语言多模态研究范畴。虽然标题提到了'Vision-Language Models'，但焦点在于模型的自我演化机制和图像处理，没有明确指向推荐系统、搜索或广告领域的潜在应用。根据排除标准，这属于'Purely Vision论文没有明确相关性到RecSys/Search/Ads'的范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 17:55:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15661v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15661v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                    Reinforcement learning (RL) provides a principled framework for improving Vision-Language Models (VLMs) on complex reasoning tasks. However, existing RL approaches often rely on human-annotated labels or task-specific heuristics to define verifiable rewards, both of which are costly and difficult to scale. We introduce VisPlay, a self-evolving RL framework that enables VLMs to autonomously improve their reasoning abilities using large amounts of unlabeled image data. Starting from a single base VLM, VisPlay assigns the model into two interacting roles: an Image-Conditioned Questioner that formulates challenging yet answerable visual questions, and a Multimodal Reasoner that generates silver responses. These roles are jointly trained with Group Relative Policy Optimization (GRPO), which incorporates diversity and difficulty rewards to balance the complexity of generated questions with the quality of the silver answers. VisPlay scales efficiently across two model families. When trained on Qwen2.5-VL and MiMo-VL, VisPlay achieves consistent improvements in visual reasoning, compositional generalization, and hallucination reduction across eight benchmarks, including MM-Vet and MMMU, demonstrating a scalable path toward self-evolving multimodal intelligence. The project page is available at https://bruno686.github.io/VisPlay/
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            <a href="https://www.alphaxiv.org/abs/2511.15605v1" target="_blank" rel="noopener noreferrer">
                SRPO：视觉-语言-动作模型的自参考策略优化
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            SRPO: Self-Referential Policy Optimization for Vision-Language-Action Models
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Senyu Fei, Siyin Wang, Li Ji, Ao Li, Shiduo Zhang, Liming Liu, Jinlong Hou, Jing...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉-语言-动作模型的策略优化，属于机器人技术和具身AI领域。虽然标题提到视觉-语言模型，但核心焦点是动作策略优化，与推荐系统、搜索或广告的排名和建模任务没有直接关联。自参考策略优化方法在推荐/搜索领域的潜在应用不明确。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 16:52:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15605v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15605v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.CV</span></div>
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                    Vision-Language-Action (VLA) models excel in robotic manipulation but are constrained by their heavy reliance on expert demonstrations, leading to demonstration bias and limiting performance. Reinforcement learning (RL) is a vital post-training strategy to overcome these limits, yet current VLA-RL methods, including group-based optimization approaches, are crippled by severe reward sparsity. Relying on binary success indicators wastes valuable information in failed trajectories, resulting in low training efficiency. To solve this, we propose Self-Referential Policy Optimization (SRPO), a novel VLA-RL framework. SRPO eliminates the need for external demonstrations or manual reward engineering by leveraging the model's own successful trajectories, generated within the current training batch, as a self-reference. This allows us to assign a progress-wise reward to failed attempts. A core innovation is the use of latent world representations to measure behavioral progress robustly. Instead of relying on raw pixels or requiring domain-specific fine-tuning, we utilize the compressed, transferable encodings from a world model's latent space. These representations naturally capture progress patterns across environments, enabling accurate, generalized trajectory comparison. Empirical evaluations on the LIBERO benchmark demonstrate SRPO's efficiency and effectiveness. Starting from a supervised baseline with 48.9% success, SRPO achieves a new state-of-the-art success rate of 99.2% in just 200 RL steps, representing a 103% relative improvement without any extra supervision. Furthermore, SRPO shows substantial robustness, achieving a 167% performance improvement on the LIBERO-Plus benchmark.
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            <a href="https://www.alphaxiv.org/abs/2511.15567v1" target="_blank" rel="noopener noreferrer">
                作为生成式用户界面评判者的计算机使用代理
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Computer-Use Agents as Judges for Generative User Interface
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kevin Qinghong Lin, Siyuan Hu, Linjie Li, Zhengyuan Yang, Lijuan Wang, Philip To...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注生成式用户界面的评估代理，属于人机交互和界面生成领域。虽然涉及生成式技术，但缺乏与推荐系统、搜索或广告的直接关联，也没有明确展示在异构数据处理或Transformer架构方面的创新应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 16:00:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15567v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15567v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.HC</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Computer-Use Agents (CUA) are becoming increasingly capable of autonomously operating digital environments through Graphical User Interfaces (GUI). Yet, most GUI remain designed primarily for humans--prioritizing aesthetics and usability--forcing agents to adopt human-oriented behaviors that are unnecessary for efficient task execution. At the same time, rapid advances in coding-oriented language models (Coder) have transformed automatic GUI design. This raises a fundamental question: Can CUA as judges to assist Coder for automatic GUI design? To investigate, we introduce AUI-Gym, a benchmark for Automatic GUI development spanning 52 applications across diverse domains. Using language models, we synthesize 1560 tasks that simulate real-world scenarios. To ensure task reliability, we further develop a verifier that programmatically checks whether each task is executable within its environment. Building on this, we propose a Coder-CUA in Collaboration framework: the Coder acts as Designer, generating and revising websites, while the CUA serves as Judge, evaluating functionality and refining designs. Success is measured not by visual appearance, but by task solvability and CUA navigation success rate. To turn CUA feedback into usable guidance, we design a CUA Dashboard that compresses multi-step navigation histories into concise visual summaries, offering interpretable guidance for iterative redesign. By positioning agents as both designers and judges, our framework shifts interface design toward agent-native efficiency and reliability. Our work takes a step toward shifting agents from passive use toward active participation in digital environments. Our code and dataset are available at https://github.com/showlab/AUI.
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            <a href="https://www.alphaxiv.org/abs/2511.15552v1" target="_blank" rel="noopener noreferrer">
                俄语架构的多模态评估
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Multimodal Evaluation of Russian-language Architectures
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Artem Chervyakov, Ulyana Isaeva, Anton Emelyanov, Artem Safin, Maria Tikhonova, ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于俄语特定语言架构的评估，属于语言特定的NLP评估范畴，与推荐系统、搜索或广告的核心技术进展无关。虽然涉及多模态概念，但缺乏明确的Transformer架构创新或推荐/搜索应用潜力，主要关注语言特定的评估基准，属于被排除的纯粹NLP评估主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 15:43:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15552v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15552v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multimodal large language models (MLLMs) are currently at the center of research attention, showing rapid progress in scale and capabilities, yet their intelligence, limitations, and risks remain insufficiently understood. To address these issues, particularly in the context of the Russian language, where no multimodal benchmarks currently exist, we introduce Mera Multi, an open multimodal evaluation framework for Russian-spoken architectures. The benchmark is instruction-based and encompasses default text, image, audio, and video modalities, comprising 18 newly constructed evaluation tasks for both general-purpose models and modality-specific architectures (image-to-text, video-to-text, and audio-to-text). Our contributions include: (i) a universal taxonomy of multimodal abilities; (ii) 18 datasets created entirely from scratch with attention to Russian cultural and linguistic specificity, unified prompts, and metrics; (iii) baseline results for both closed-source and open-source models; (iv) a methodology for preventing benchmark leakage, including watermarking and licenses for private sets. While our current focus is on Russian, the proposed benchmark provides a replicable methodology for constructing multimodal benchmarks in typologically diverse languages, particularly within the Slavic language family.
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            <a href="https://www.alphaxiv.org/abs/2511.15512v1" target="_blank" rel="noopener noreferrer">
                标准化NLP工作流：可复现语言分析框架
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        <div class="mb-2 text-base text-gray-700">
            Standardising the NLP Workflow: A Framework for Reproducible Linguistic Analysis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yves Pauli, Jan-Bernard Marsman, Finn Rabe, Victoria Edkins, Roya Hüppi, Silvia ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注NLP工作流程的标准化和可复现性，属于NLP工程实践范畴。虽然LLM技术可能受益于更好的工程实践，但该论文本身不涉及核心LLM技术进展、Transformer架构创新，也没有直接应用于推荐系统、搜索或广告的具体潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 15:06:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15512v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15512v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The introduction of large language models and other influential developments in AI-based language processing have led to an evolution in the methods available to quantitatively analyse language data. With the resultant growth of attention on language processing, significant challenges have emerged, including the lack of standardisation in organising and sharing linguistic data and the absence of standardised and reproducible processing methodologies. Striving for future standardisation, we first propose the Language Processing Data Structure (LPDS), a data structure inspired by the Brain Imaging Data Structure (BIDS), a widely adopted standard for handling neuroscience data. It provides a folder structure and file naming conventions for linguistic research. Second, we introduce pelican nlp, a modular and extensible Python package designed to enable streamlined language processing, from initial data cleaning and task-specific preprocessing to the extraction of sophisticated linguistic and acoustic features, such as semantic embeddings and prosodic metrics. The entire processing workflow can be specified within a single, shareable configuration file, which pelican nlp then executes on LPDS-formatted data. Depending on the specifications, the reproducible output can consist of preprocessed language data or standardised extraction of both linguistic and acoustic features and corresponding result aggregations. LPDS and pelican nlp collectively offer an end-to-end processing pipeline for linguistic data, designed to ensure methodological transparency and enhance reproducibility.
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            <a href="https://www.alphaxiv.org/abs/2511.15370v1" target="_blank" rel="noopener noreferrer">
                大型语言模型对科学学的赋能：新工具与方法
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            The Empowerment of Science of Science by Large Language Models: New Tools and Methods
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Guoqiang Liang, Jingqian Gong, Mengxuan Li, Gege Lin, Shuo Zhang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM在科学学（Science of Science）领域的应用，这是一个与科学研究和学术分析相关的专门领域。虽然涉及LLM技术，但其应用场景与推荐系统、搜索或广告没有直接关联，也不涉及这些领域所需的核心技术进展或架构创新。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 11:57:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15370v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15370v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Large language models (LLMs) have exhibited exceptional capabilities in natural language understanding and generation, image recognition, and multimodal tasks, charting a course towards AGI and emerging as a central issue in the global technological race. This manuscript conducts a comprehensive review of the core technologies that support LLMs from a user standpoint, including prompt engineering, knowledge-enhanced retrieval augmented generation, fine tuning, pretraining, and tool learning. Additionally, it traces the historical development of Science of Science (SciSci) and presents a forward looking perspective on the potential applications of LLMs within the scientometric domain. Furthermore, it discusses the prospect of an AI agent based model for scientific evaluation, and presents new research fronts detection and knowledge graph building methods with LLMs.
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            <a href="https://www.alphaxiv.org/abs/2511.15291v1" target="_blank" rel="noopener noreferrer">
                MAPROC在AHaSIS共享任务中的研究：基于少样本学习和句子Transformer的阿拉伯语酒店评论情感分析
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        <div class="mb-2 text-base text-gray-700">
            MAPROC at AHaSIS Shared Task: Few-Shot and Sentence Transformer for Sentiment Analysis of Arabic Hotel Reviews
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Randa Zarnoufi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注阿拉伯语酒店评论的情感分析，这属于特定语言和领域的NLP应用。虽然使用了Transformer技术，但应用场景（酒店评论情感分析）与推荐系统、搜索或广告的核心排名和匹配任务关联度较低。情感分析可能作为辅助特征用于推荐系统，但这不是直接的核心应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 09:56:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15291v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15291v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Sentiment analysis of Arabic dialects presents significant challenges due to linguistic diversity and the scarcity of annotated data. This paper describes our approach to the AHaSIS shared task, which focuses on sentiment analysis on Arabic dialects in the hospitality domain. The dataset comprises hotel reviews written in Moroccan and Saudi dialects, and the objective is to classify the reviewers sentiment as positive, negative, or neutral. We employed the SetFit (Sentence Transformer Fine-tuning) framework, a data-efficient few-shot learning technique. On the official evaluation set, our system achieved an F1 of 73%, ranking 12th among 26 participants. This work highlights the potential of few-shot learning to address data scarcity in processing nuanced dialectal Arabic text within specialized domains like hotel reviews.
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            <a href="https://www.alphaxiv.org/abs/2511.15257v1" target="_blank" rel="noopener noreferrer">
                M：可复用模型编译的工具链与语言
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            M, Toolchain and Language for Reusable Model Compilation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hiep Hong Trinh, Federico Ciccozzi, Abu Naser Masud, Marjan Sirjani, Mikael Sjöd...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注模型编译工具链和语言，属于底层系统优化范畴，与推荐系统、搜索或广告的核心算法进展关联较弱。虽然高效的模型编译可能间接提升推理性能，但论文未明确涉及Transformer架构、LLM技术或推荐/搜索/广告领域的直接应用场景，因此相关性有限。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 09:21:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15257v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15257v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.SE</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Complex software-driven systems often interleave distributed, concurrent computation processes with physical interactions with the environment. Developing these systems more efficiently and safely can be achieved by employing actionable, software-based models. From a high-level system model, engineers often need to derive multiple specialized models for different purposes, including simulation, deployment, and formal verification. Each of these target models usually rely on its own formalism, specification language, and execution platform. Traditionally, a compiler analyzes a program written in a programming language and generates executable code. In contrast, a model compiler processes a source model written in a modeling language and should ideally support the generation of multiple heterogeneous targets. However, most existing modeling languages are designed with a narrow focus, typically targeting only simulation or implementation. Multi-target compilation, when not considered during the language's early design, becomes significantly harder to achieve. In this paper, we introduce our initiative: a toolchain and modeling language called M, designed to support system modeling and multi-target compilation for model-driven engineering of complex, concurrent, and time-aware systems. M is a textual, grammar-driven language based on the actor model and extended with discrete-event scheduling semantics. It provides constructs for modeling system entities, message-based interactions, and time- or state-triggered reactions. From such models, M enables the systematic generation of diverse target artifacts while preserving semantic conformance to the original model. Moreover, M can serve as a middle language to which other modeling languages may anchor, thereby allowing them to benefit from its compilation framework.
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            <a href="https://www.alphaxiv.org/abs/2511.15210v1" target="_blank" rel="noopener noreferrer">
                揭示文本的内在维度：从学术摘要到创意故事
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            Unveiling Intrinsic Dimension of Texts: from Academic Abstract to Creative Story
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Vladislav Pedashenko, Laida Kushnareva, Yana Khassan Nibal, Eduard Tulchinskii, ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注文本内在维度的测量和分析，属于基础NLP研究范畴。虽然文本理解是搜索系统的基础组件，但论文标题显示其焦点是通用的文本维度分析，并未明确指向推荐、搜索或广告应用，也未涉及Transformer架构改进或LLM技术进展。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 08:00:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15210v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15210v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                    Intrinsic dimension (ID) is an important tool in modern LLM analysis, informing studies of training dynamics, scaling behavior, and dataset structure, yet its textual determinants remain underexplored. We provide the first comprehensive study grounding ID in interpretable text properties through cross-encoder analysis, linguistic features, and sparse autoencoders (SAEs). In this work, we establish three key findings. First, ID is complementary to entropy-based metrics: after controlling for length, the two are uncorrelated, with ID capturing geometric complexity orthogonal to prediction quality. Second, ID exhibits robust genre stratification: scientific prose shows low ID (~8), encyclopedic content medium ID (~9), and creative/opinion writing high ID (~10.5) across all models tested. This reveals that contemporary LLMs find scientific text "representationally simple" while fiction requires additional degrees of freedom. Third, using SAEs, we identify causal features: scientific signals (formal tone, report templates, statistics) reduce ID; humanized signals (personalization, emotion, narrative) increase it. Steering experiments confirm these effects are causal. Thus, for contemporary models, scientific writing appears comparatively "easy", whereas fiction, opinion, and affect add representational degrees of freedom. Our multi-faceted analysis provides practical guidance for the proper use of ID and the sound interpretation of ID-based results.
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            <a href="https://www.alphaxiv.org/abs/2511.15163v1" target="_blank" rel="noopener noreferrer">
                因材施教：基于用户画像、记忆与遗忘感知大语言模型的个性化数学辅导
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            Teaching According to Students' Aptitude: Personalized Mathematics Tutoring via Persona-, Memory-, and Forgetting-Aware LLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yang Wu, Rujing Yao, Tong Zhang, Yufei Shi, Zhuoren Jiang, Zhushan Li, Xiaozhong...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注教育领域的个性化辅导应用，属于特定领域应用而非核心推荐系统、搜索或广告技术。虽然涉及个性化建模和记忆机制，但这些技术在教育场景中的应用与RecSys/Search/Ads的商业应用场景关联度较低，缺乏明确的跨领域技术迁移潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 06:28:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15163v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15163v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.HC</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) are increasingly integrated into intelligent tutoring systems to provide human-like and adaptive instruction. However, most existing approaches fail to capture how students' knowledge evolves dynamically across their proficiencies, conceptual gaps, and forgetting patterns. This challenge is particularly acute in mathematics tutoring, where effective instruction requires fine-grained scaffolding precisely calibrated to each student's mastery level and cognitive retention. To address this issue, we propose TASA (Teaching According to Students' Aptitude), a student-aware tutoring framework that integrates persona, memory, and forgetting dynamics for personalized mathematics learning. Specifically, TASA maintains a structured student persona capturing proficiency profiles and an event memory recording prior learning interactions. By incorporating a continuous forgetting curve with knowledge tracing, TASA dynamically updates each student's mastery state and generates contextually appropriate, difficulty-calibrated questions and explanations. Empirical results demonstrate that TASA achieves superior learning outcomes and more adaptive tutoring behavior compared to representative baselines, underscoring the importance of modeling temporal forgetting and learner profiles in LLM-based tutoring systems.
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            <a href="https://www.alphaxiv.org/abs/2511.15069v1" target="_blank" rel="noopener noreferrer">
                ProRAC：一种基于LLM进展的神经符号方法，用于动作推理
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            ProRAC: A Neuro-symbolic Method for Reasoning about Actions with LLM-based Progression
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haoyong Wu, Yongmei Liu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注神经符号方法和动作推理，属于通用推理技术领域。虽然涉及LLM技术，但缺乏明确的与推荐系统、搜索或广告相关的应用场景。动作推理在RecSys/Search/Ads中的潜在应用（如用户行为序列建模）不够直接和明确。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 03:20:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15069v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15069v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                    In this paper, we propose ProRAC (Progression-based Reasoning about Actions and Change), a neuro-symbolic framework that leverages LLMs to tackle RAC problems. ProRAC extracts fundamental RAC elements including actions and questions from the problem, progressively executes each action to derive the final state, and then evaluates the query against the progressed state to arrive at an answer. We evaluate ProRAC on several RAC benchmarks, and the results demonstrate that our approach achieves strong performance across different benchmarks, domains, LLM backbones, and types of RAC tasks.
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            <a href="https://www.alphaxiv.org/abs/2511.15059v1" target="_blank" rel="noopener noreferrer">
                评估多模态大语言模型在竖排日文文本上的表现
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            Evaluating Multimodal Large Language Models on Vertically Written Japanese Text
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Keito Sasagawa, Shuhei Kurita, Daisuke Kawahara
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多模态LLM在特定语言格式（竖排日文）上的评估，这属于纯粹的NLP评估基准研究。虽然涉及多模态和LLM技术，但缺乏明确的推荐系统、搜索或广告应用场景，且评估焦点与我的技术应用导向不匹配。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 03:04:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15059v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15059v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multimodal Large Language Models (MLLMs) have seen rapid advances in recent years and are now being applied to visual document understanding tasks. They are expected to process a wide range of document images across languages, including Japanese. Understanding documents from images requires models to read what are written in them. Since some Japanese documents are written vertically, support for vertical writing is essential. However, research specifically focused on vertically written Japanese text remains limited. In this study, we evaluate the reading capability of existing MLLMs on vertically written Japanese text. First, we generate a synthetic Japanese OCR dataset by rendering Japanese texts into images, and use it for both model fine-tuning and evaluation. This dataset includes Japanese text in both horizontal and vertical writing. We also create an evaluation dataset sourced from the real-world document images containing vertically written Japanese text. Using these datasets, we demonstrate that the existing MLLMs perform worse on vertically written Japanese text than on horizontally written Japanese text. Furthermore, we show that training MLLMs on our synthesized Japanese OCR dataset results in improving the performance of models that previously could not handle vertical writing. The datasets and code are publicly available https://github.com/llm-jp/eval_vertical_ja.
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            <a href="https://www.alphaxiv.org/abs/2511.15705v1" target="_blank" rel="noopener noreferrer">
                GeoVista：用于地理定位的Web增强代理视觉推理
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            GeoVista: Web-Augmented Agentic Visual Reasoning for Geolocalization
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yikun Wang, Zuyan Liu, Ziyi Wang, Pengfei Liu, Han Hu, Yongming Rao
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注地理定位任务中的视觉推理，这属于计算机视觉的特定应用领域。虽然提到了Web增强和代理推理，但这些技术主要服务于地理定位这一特定任务，与推荐系统、搜索或广告的核心技术栈没有明确的直接关联。论文没有显示出在Transformer架构、LLM技术或异构数据统一建模方面的创新，而这些才是当前关注的重点领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 18:59:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15705v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15705v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Current research on agentic visual reasoning enables deep multimodal understanding but primarily focuses on image manipulation tools, leaving a gap toward more general-purpose agentic models. In this work, we revisit the geolocalization task, which requires not only nuanced visual grounding but also web search to confirm or refine hypotheses during reasoning. Since existing geolocalization benchmarks fail to meet the need for high-resolution imagery and the localization challenge for deep agentic reasoning, we curate GeoBench, a benchmark that includes photos and panoramas from around the world, along with a subset of satellite images of different cities to rigorously evaluate the geolocalization ability of agentic models. We also propose GeoVista, an agentic model that seamlessly integrates tool invocation within the reasoning loop, including an image-zoom-in tool to magnify regions of interest and a web-search tool to retrieve related web information. We develop a complete training pipeline for it, including a cold-start supervised fine-tuning (SFT) stage to learn reasoning patterns and tool-use priors, followed by a reinforcement learning (RL) stage to further enhance reasoning ability. We adopt a hierarchical reward to leverage multi-level geographical information and improve overall geolocalization performance. Experimental results show that GeoVista surpasses other open-source agentic models on the geolocalization task greatly and achieves performance comparable to closed-source models such as Gemini-2.5-flash and GPT-5 on most metrics.
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                首帧是视频内容定制的最佳切入点
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            First Frame Is the Place to Go for Video Content Customization
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jingxi Chen, Zongxia Li, Zhichao Liu, Guangyao Shi, Xiyang Wu, Fuxiao Liu, Corne...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于视频内容定制，主要涉及视频处理而非推荐系统、搜索或广告的核心领域。虽然视频推荐是推荐系统的一个子领域，但标题强调内容定制而非排名或检索，与当前关注的LLM技术、Transformer架构进展或异构数据统一建模的直接应用关联较弱。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 18:56:50
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15700v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15700v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    What role does the first frame play in video generation models? Traditionally, it's viewed as the spatial-temporal starting point of a video, merely a seed for subsequent animation. In this work, we reveal a fundamentally different perspective: video models implicitly treat the first frame as a conceptual memory buffer that stores visual entities for later reuse during generation. Leveraging this insight, we show that it's possible to achieve robust and generalized video content customization in diverse scenarios, using only 20-50 training examples without architectural changes or large-scale finetuning. This unveils a powerful, overlooked capability of video generation models for reference-based video customization.
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            <a href="https://www.alphaxiv.org/abs/2511.15656v1" target="_blank" rel="noopener noreferrer">
                INQUIRE-Search：大规模生物多样性数据库中交互式发现的框架
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            INQUIRE-Search: A Framework for Interactive Discovery in Large-Scale Biodiversity Databases
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Edward Vendrow, Julia Chae, Rupa Kurinchi-Vendhan, Isaac Eckert, Jazlynn Hall, M...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文虽然涉及搜索框架和交互式发现，但其核心应用领域是生物多样性数据库，属于明确的生物学领域特定应用。根据用户明确的排除标准，生物学等特定领域应用属于不相关主题，因此与用户关注的推荐系统、搜索广告等核心领域相关性极低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 17:42:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15656v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15656v1
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large community science platforms such as iNaturalist contain hundreds of millions of biodiversity images that often capture ecological context on behaviors, interactions, phenology, and habitat. Yet most ecological workflows rely on metadata filtering or manual inspection, leaving this secondary information inaccessible at scale. We introduce INQUIRE-Search, an open-source system that enables scientists to rapidly and interactively search within an ecological image database for specific concepts using natural language, verify and export relevant observations, and utilize this discovered data for novel scientific analysis. Compared to traditional methods, INQUIRE-Search takes a fraction of the time, opening up new possibilities for scientific questions that can be explored. Through five case studies, we show the diversity of scientific applications that a tool like INQUIRE-Search can support, from seasonal variation in behavior across species to forest regrowth after wildfires. These examples demonstrate a new paradigm for interactive, efficient, and scalable scientific discovery that can begin to unlock previously inaccessible scientific value in large-scale biodiversity datasets. Finally, we emphasize using such AI-enabled discovery tools for science call for experts to reframe the priorities of the scientific process and develop novel methods for experiment design, data collection, survey effort, and uncertainty analysis.
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            <a href="https://www.alphaxiv.org/abs/2511.15633v1" target="_blank" rel="noopener noreferrer">
                基于CLIP的类增量学习的分层语义树锚定
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Hierarchical Semantic Tree Anchoring for CLIP-Based Class-Incremental Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tao Hu, Lan Li, Zhen-Hao Xie, Da-Wei Zhou
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉领域的类增量学习，虽然使用了CLIP模型，但其核心应用场景是视觉分类任务，与推荐系统、搜索或广告的关联性较弱。CLIP技术本身具有跨模态潜力，但论文专注于视觉分类的增量学习问题，缺乏明确的RecSys/Search/Ads应用前景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 17:14:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15633v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15633v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Class-Incremental Learning (CIL) enables models to learn new classes continually while preserving past knowledge. Recently, vision-language models like CLIP offer transferable features via multi-modal pre-training, making them well-suited for CIL. However, real-world visual and linguistic concepts are inherently hierarchical: a textual concept like "dog" subsumes fine-grained categories such as "Labrador" and "Golden Retriever," and each category entails its images. But existing CLIP-based CIL methods fail to explicitly capture this inherent hierarchy, leading to fine-grained class features drift during incremental updates and ultimately to catastrophic forgetting. To address this challenge, we propose HASTEN (Hierarchical Semantic Tree Anchoring) that anchors hierarchical information into CIL to reduce catastrophic forgetting. First, we employ an external knowledge graph as supervision to embed visual and textual features in hyperbolic space, effectively preserving hierarchical structure as data evolves. Second, to mitigate catastrophic forgetting, we project gradients onto the null space of the shared hyperbolic mapper, preventing interference with prior tasks. These two steps work synergistically to enable the model to resist forgetting by maintaining hierarchical relationships. Extensive experiments show that HASTEN consistently outperforms existing methods while providing a unified structured representation.
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            <a href="https://www.alphaxiv.org/abs/2511.15515v1" target="_blank" rel="noopener noreferrer">
                多文本引导的少样本语义分割
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        <div class="mb-2 text-base text-gray-700">
            Multi-Text Guided Few-Shot Semantic Segmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qiang Jiao, Bin Yan, Yi Yang, Mengrui Shi, Qiang Zhang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉领域的语义分割任务，属于纯粹的视觉研究方向。虽然提到了多文本引导，但这与推荐系统、搜索或广告中的异构数据处理没有直接关联。该技术缺乏在RecSys/Search/Ads领域的明确应用场景，属于被排除的纯视觉研究范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 15:09:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15515v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15515v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent CLIP-based few-shot semantic segmentation methods introduce class-level textual priors to assist segmentation by typically using a single prompt (e.g., a photo of class). However, these approaches often result in incomplete activation of target regions, as a single textual description cannot fully capture the semantic diversity of complex categories. Moreover, they lack explicit cross-modal interaction and are vulnerable to noisy support features, further degrading visual prior quality. To address these issues, we propose the Multi-Text Guided Few-Shot Semantic Segmentation Network (MTGNet), a dual-branch framework that enhances segmentation performance by fusing diverse textual prompts to refine textual priors and guide the cross-modal optimization of visual priors. Specifically, we design a Multi-Textual Prior Refinement (MTPR) module that suppresses interference and aggregates complementary semantic cues to enhance foreground activation and expand semantic coverage for structurally complex objects. We introduce a Text Anchor Feature Fusion (TAFF) module, which leverages multi-text embeddings as semantic anchors to facilitate the transfer of discriminative local prototypes from support images to query images, thereby improving semantic consistency and alleviating intra-class variations. Furthermore, a Foreground Confidence-Weighted Attention (FCWA) module is presented to enhance visual prior robustness by leveraging internal self-similarity within support foreground features. It adaptively down-weights inconsistent regions and effectively suppresses interference in the query segmentation process. Extensive experiments on standard FSS benchmarks validate the effectiveness of MTGNet. In the 1-shot setting, it achieves 76.8% mIoU on PASCAL-5i and 57.4% on COCO-20i, with notable improvements in folds exhibiting high intra-class variations.
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            <a href="https://www.alphaxiv.org/abs/2511.15499v1" target="_blank" rel="noopener noreferrer">
                学习扩展图像以实现高效的视觉自回归建模
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        <div class="mb-2 text-base text-gray-700">
            Learning to Expand Images for Efficient Visual Autoregressive Modeling
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ruiqing Yang, Kaixin Zhang, Zheng Zhang, Shan You, Tao Huang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉自回归建模和图像扩展技术，属于纯粹的计算机视觉领域。虽然自回归建模在LLM中有应用，但本文专注于视觉任务，没有明确与推荐系统、搜索或广告的关联。其技术可能间接应用于多模态推荐中的图像处理，但直接相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 14:55:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15499v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15499v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Autoregressive models have recently shown great promise in visual generation by leveraging discrete token sequences akin to language modeling. However, existing approaches often suffer from inefficiency, either due to token-by-token decoding or the complexity of multi-scale representations. In this work, we introduce Expanding Autoregressive Representation (EAR), a novel generation paradigm that emulates the human visual system's center-outward perception pattern. EAR unfolds image tokens in a spiral order from the center and progressively expands outward, preserving spatial continuity and enabling efficient parallel decoding. To further enhance flexibility and speed, we propose a length-adaptive decoding strategy that dynamically adjusts the number of tokens predicted at each step. This biologically inspired design not only reduces computational cost but also improves generation quality by aligning the generation order with perceptual relevance. Extensive experiments on ImageNet demonstrate that EAR achieves state-of-the-art trade-offs between fidelity and efficiency on single-scale autoregressive models, setting a new direction for scalable and cognitively aligned autoregressive image generation.
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            <a href="https://www.alphaxiv.org/abs/2511.15487v1" target="_blank" rel="noopener noreferrer">
                NTK引导的隐式神经教学
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            NTK-Guided Implicit Neural Teaching
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chen Zhang, Wei Zuo, Bingyang Cheng, Yikun Wang, Wei-Bin Kou, Yik Chung WU, Ngai...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题暗示涉及神经正切核(NTK)理论和隐式神经表示的教学方法，这属于神经网络理论优化领域。虽然NTK理论可能对理解Transformer训练动态有理论价值，但缺乏明确的与推荐系统、搜索或广告的具体应用连接。该工作更偏向于理论神经网络分析而非实际的推荐/搜索系统改进。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 14:43:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15487v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15487v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
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                    Implicit Neural Representations (INRs) parameterize continuous signals via multilayer perceptrons (MLPs), enabling compact, resolution-independent modeling for tasks like image, audio, and 3D reconstruction. However, fitting high-resolution signals demands optimizing over millions of coordinates, incurring prohibitive computational costs. To address it, we propose NTK-Guided Implicit Neural Teaching (NINT), which accelerates training by dynamically selecting coordinates that maximize global functional updates. Leveraging the Neural Tangent Kernel (NTK), NINT scores examples by the norm of their NTK-augmented loss gradients, capturing both fitting errors and heterogeneous leverage (self-influence and cross-coordinate coupling). This dual consideration enables faster convergence compared to existing methods. Through extensive experiments, we demonstrate that NINT significantly reduces training time by nearly half while maintaining or improving representation quality, establishing state-of-the-art acceleration among recent sampling-based strategies.
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                通过保形预测控制图像分割中的假阳性
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            Controlling False Positives in Image Segmentation via Conformal Prediction
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Luca Mossina, Corentin Friedrich
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的图像分割和不确定性量化方法（保形预测），与推荐系统、搜索或广告的核心技术栈没有直接关联。虽然保形预测在理论上可以应用于推荐系统中的置信度校准，但论文的视觉分割焦点使其实际应用价值有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 13:02:50
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15406v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15406v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Reliable semantic segmentation is essential for clinical decision making, yet deep models rarely provide explicit statistical guarantees on their errors. We introduce a simple post-hoc framework that constructs confidence masks with distribution-free, image-level control of false-positive predictions. Given any pretrained segmentation model, we define a nested family of shrunken masks obtained either by increasing the score threshold or by applying morphological erosion. A labeled calibration set is used to select a single shrink parameter via conformal prediction, ensuring that, for new images that are exchangeable with the calibration data, the proportion of false positives retained in the confidence mask stays below a user-specified tolerance with high probability. The method is model-agnostic, requires no retraining, and provides finite-sample guarantees regardless of the underlying predictor. Experiments on a polyp-segmentation benchmark demonstrate target-level empirical validity. Our framework enables practical, risk-aware segmentation in settings where over-segmentation can have clinical consequences. Code at https://github.com/deel-ai-papers/conseco.
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            <a href="https://www.alphaxiv.org/abs/2511.15396v1" target="_blank" rel="noopener noreferrer">
                ShelfOcc：超越激光雷达的视觉占用估计原生3D监督
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            ShelfOcc: Native 3D Supervision beyond LiDAR for Vision-Based Occupancy Estimation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Simon Boeder, Fabian Gigengack, Simon Roesler, Holger Caesar, Benjamin Risse
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的3D占用估计技术，主要涉及自动驾驶场景的3D感知。虽然标题提到'超越LiDAR'可能暗示效率改进，但这是纯粹的视觉/3D视觉研究，没有明确与推荐系统、搜索或广告的关联。视觉占用估计在RecSys/Search/Ads领域缺乏直接应用场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 12:44:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15396v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15396v1
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                    Recent progress in self- and weakly supervised occupancy estimation has largely relied on 2D projection or rendering-based supervision, which suffers from geometric inconsistencies and severe depth bleeding. We thus introduce ShelfOcc, a vision-only method that overcomes these limitations without relying on LiDAR. ShelfOcc brings supervision into native 3D space by generating metrically consistent semantic voxel labels from video, enabling true 3D supervision without any additional sensors or manual 3D annotations. While recent vision-based 3D geometry foundation models provide a promising source of prior knowledge, they do not work out of the box as a prediction due to sparse or noisy and inconsistent geometry, especially in dynamic driving scenes. Our method introduces a dedicated framework that mitigates these issues by filtering and accumulating static geometry consistently across frames, handling dynamic content and propagating semantic information into a stable voxel representation. This data-centric shift in supervision for weakly/shelf-supervised occupancy estimation allows the use of essentially any SOTA occupancy model architecture without relying on LiDAR data. We argue that such high-quality supervision is essential for robust occupancy learning and constitutes an important complementary avenue to architectural innovation. On the Occ3D-nuScenes benchmark, ShelfOcc substantially outperforms all previous weakly/shelf-supervised methods (up to a 34% relative improvement), establishing a new data-driven direction for LiDAR-free 3D scene understanding.
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            <a href="https://www.alphaxiv.org/abs/2511.15379v1" target="_blank" rel="noopener noreferrer">
                基于测试时训练实现零样本开放词汇人体运动定位
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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            Zero-Shot Open-Vocabulary Human Motion Grounding with Test-Time Training
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yunjiao Zhou, Xinyan Chen, Junlang Qian, Lihua Xie, Jianfei Yang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉中的人体运动定位任务，属于纯粹的视觉领域研究。虽然涉及零样本和开放词汇等概念，但其核心应用场景是视觉理解而非推荐系统、搜索或广告。该技术缺乏明确的路径应用于异构数据建模或推荐系统相关任务。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 12:11:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15379v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15379v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Understanding complex human activities demands the ability to decompose motion into fine-grained, semantic-aligned sub-actions. This motion grounding process is crucial for behavior analysis, embodied AI and virtual reality. Yet, most existing methods rely on dense supervision with predefined action classes, which are infeasible in open-vocabulary, real-world settings. In this paper, we propose ZOMG, a zero-shot, open-vocabulary framework that segments motion sequences into semantically meaningful sub-actions without requiring any annotations or fine-tuning. Technically, ZOMG integrates (1) language semantic partition, which leverages large language models to decompose instructions into ordered sub-action units, and (2) soft masking optimization, which learns instance-specific temporal masks to focus on frames critical to sub-actions, while maintaining intra-segment continuity and enforcing inter-segment separation, all without altering the pretrained encoder. Experiments on three motion-language datasets demonstrate state-of-the-art effectiveness and efficiency of motion grounding performance, outperforming prior methods by +8.7\% mAP on HumanML3D benchmark. Meanwhile, significant improvements also exist in downstream retrieval, establishing a new paradigm for annotation-free motion understanding.
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            <a href="https://www.alphaxiv.org/abs/2511.15312v1" target="_blank" rel="noopener noreferrer">
                基于雷达、音频和视频数据的无人机检测与空中目标识别多模态Transformer方法
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            A Multimodal Transformer Approach for UAV Detection and Aerial Object Recognition Using Radar, Audio, and Video Data
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mauro Larrat, Claudomiro Sales
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文虽然涉及多模态Transformer架构，但其应用领域是无人机检测和空中目标识别，属于计算机视觉和传感器融合的特定应用。与搜索、推荐或广告系统没有直接关联，多模态Transformer技术在此处的应用场景过于特定化，难以迁移到推荐系统或搜索广告领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 10:22:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15312v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15312v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Unmanned aerial vehicle (UAV) detection and aerial object recognition are critical for modern surveillance and security, prompting a need for robust systems that overcome limitations of single-modality approaches. This research addresses these challenges by designing and rigorously evaluating a novel multimodal Transformer model that integrates diverse data streams: radar, visual band video (RGB), infrared (IR) video, and audio. The architecture effectively fuses distinct features from each modality, leveraging the Transformer's self-attention mechanisms to learn comprehensive, complementary, and highly discriminative representations for classification. The model demonstrated exceptional performance on an independent test set, achieving macro-averaged metrics of 0.9812 accuracy, 0.9873 recall, 0.9787 precision, 0.9826 F1-score, and 0.9954 specificity. Notably, it exhibited particularly high precision and recall in distinguishing drones from other aerial objects. Furthermore, computational analysis confirmed its efficiency, with 1.09 GFLOPs, 1.22 million parameters, and an inference speed of 41.11 FPS, highlighting its suitability for real-time applications. This study presents a significant advancement in aerial object classification, validating the efficacy of multimodal data fusion via a Transformer architecture for achieving state-of-the-art performance, thereby offering a highly accurate and resilient solution for UAV detection and monitoring in complex airspace.
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            <a href="https://www.alphaxiv.org/abs/2511.15311v1" target="_blank" rel="noopener noreferrer">
                云端漫步即适应：三维视觉语言基础模型的免训练在线测试时自适应
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            Adapt-As-You-Walk Through the Clouds: Training-Free Online Test-Time Adaptation of 3D Vision-Language Foundation Models
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mehran Tamjidi, Hamidreza Dastmalchi, Mohammadreza Alimoradijazi, Ali Cheraghian...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注3D视觉语言模型的在线测试时自适应技术，属于纯粹的视觉领域研究。虽然提到了基础模型和自适应概念，但3D视觉的特定应用场景与推荐系统、搜索或广告的核心需求缺乏直接关联，且没有明确展示这些技术如何转化到异构数据处理或推荐排序任务中。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 10:22:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15311v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15311v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    3D Vision-Language Foundation Models (VLFMs) have shown strong generalization and zero-shot recognition capabilities in open-world point cloud processing tasks. However, these models often underperform in practical scenarios where data are noisy, incomplete, or drawn from a different distribution than the training data. To address this, we propose Uni-Adapter, a novel training-free online test-time adaptation (TTA) strategy for 3D VLFMs based on dynamic prototype learning. We define a 3D cache to store class-specific cluster centers as prototypes, which are continuously updated to capture intra-class variability in heterogeneous data distributions. These dynamic prototypes serve as anchors for cache-based logit computation via similarity scoring. Simultaneously, a graph-based label smoothing module captures inter-prototype similarities to enforce label consistency among similar prototypes. Finally, we unify predictions from the original 3D VLFM and the refined 3D cache using entropy-weighted aggregation for reliable adaptation. Without retraining, Uni-Adapter effectively mitigates distribution shifts, achieving state-of-the-art performance on diverse 3D benchmarks over different 3D VLFMs, improving ModelNet-40C by 10.55%, ScanObjectNN-C by 8.26%, and ShapeNet-C by 4.49% over the source 3D VLFMs.
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            <a href="https://www.alphaxiv.org/abs/2511.15308v1" target="_blank" rel="noopener noreferrer">
                Text2Loc++：从自然语言泛化三维点云定位
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            Text2Loc++: Generalizing 3D Point Cloud Localization from Natural Language
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yan Xia, Letian Shi, Yilin Di, Joao F. Henriques, Daniel Cremers
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注3D点云定位与自然语言的结合，属于计算机视觉和3D感知领域，与搜索/推荐/广告的核心技术关联度极低。虽然涉及自然语言处理，但其应用场景（3D定位）与文本搜索、内容推荐或广告排名没有直接联系，也不属于Transformer架构改进或异构数据统一建模的相关研究方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 10:19:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15308v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15308v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    We tackle the problem of localizing 3D point cloud submaps using complex and diverse natural language descriptions, and present Text2Loc++, a novel neural network designed for effective cross-modal alignment between language and point clouds in a coarse-to-fine localization pipeline. To support benchmarking, we introduce a new city-scale dataset covering both color and non-color point clouds from diverse urban scenes, and organize location descriptions into three levels of linguistic complexity. In the global place recognition stage, Text2Loc++ combines a pretrained language model with a Hierarchical Transformer with Max pooling (HTM) for sentence-level semantics, and employs an attention-based point cloud encoder for spatial understanding. We further propose Masked Instance Training (MIT) to filter out non-aligned objects and improve multimodal robustness. To enhance the embedding space, we introduce Modality-aware Hierarchical Contrastive Learning (MHCL), incorporating cross-modal, submap-, text-, and instance-level losses. In the fine localization stage, we completely remove explicit text-instance matching and design a lightweight yet powerful framework based on Prototype-based Map Cloning (PMC) and a Cascaded Cross-Attention Transformer (CCAT). Extensive experiments on the KITTI360Pose dataset show that Text2Loc++ outperforms existing methods by up to 15%. In addition, the proposed model exhibits robust generalization when evaluated on the new dataset, effectively handling complex linguistic expressions and a wide variety of urban environments. The code and dataset will be made publicly available.
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            <a href="https://www.alphaxiv.org/abs/2511.15288v1" target="_blank" rel="noopener noreferrer">
                面向三维场景图预测的边缘中心关系推理
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Edge-Centric Relational Reasoning for 3D Scene Graph Prediction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yanni Ma, Hao Liu, Yulan Guo, Theo Gevers, Martin R. Oswald
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D场景图预测，属于纯粹的计算机视觉和3D视觉领域，与推荐系统、搜索或广告的核心技术没有直接关联。虽然关系推理技术在概念上可能对某些推荐场景有启发，但论文的3D场景图焦点使其应用潜力非常有限且间接。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 09:53:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15288v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15288v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    3D scene graph prediction aims to abstract complex 3D environments into structured graphs consisting of objects and their pairwise relationships. Existing approaches typically adopt object-centric graph neural networks, where relation edge features are iteratively updated by aggregating messages from connected object nodes. However, this design inherently restricts relation representations to pairwise object context, making it difficult to capture high-order relational dependencies that are essential for accurate relation prediction. To address this limitation, we propose a Link-guided Edge-centric relational reasoning framework with Object-aware fusion, namely LEO, which enables progressive reasoning from relation-level context to object-level understanding. Specifically, LEO first predicts potential links between object pairs to suppress irrelevant edges, and then transforms the original scene graph into a line graph where each relation is treated as a node. A line graph neural network is applied to perform edge-centric relational reasoning to capture inter-relation context. The enriched relation features are subsequently integrated into the original object-centric graph to enhance object-level reasoning and improve relation prediction. Our framework is model-agnostic and can be integrated with any existing object-centric method. Experiments on the 3DSSG dataset with two competitive baselines show consistent improvements, highlighting the effectiveness of our edge-to-object reasoning paradigm.
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            <a href="https://www.alphaxiv.org/abs/2511.15258v1" target="_blank" rel="noopener noreferrer">
                SplitFlux：从单张图像中学习解耦内容与风格
            </a>
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            SplitFlux: Learning to Decouple Content and Style from a Single Image
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yitong Yang, Yinglin Wang, Changshuo Wang, Yongjun Zhang, Ziyang Chen, Shuting H...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的内容-风格解耦技术，虽然解耦表示学习在推荐系统中可能有间接应用，但论文标题明确限定于单张图像处理，与RecSys/Search/Ads的核心技术关联度较低。没有明确的机制表明这项技术能直接应用于处理推荐系统中的异构数据或改进排序模型。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 09:22:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15258v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15258v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Disentangling image content and style is essential for customized image generation. Existing SDXL-based methods struggle to achieve high-quality results, while the recently proposed Flux model fails to achieve effective content-style separation due to its underexplored characteristics. To address these challenges, we conduct a systematic analysis of Flux and make two key observations: (1) Single Dream Blocks are essential for image generation; and (2) Early single stream blocks mainly control content, whereas later blocks govern style. Based on these insights, we propose SplitFlux, which disentangles content and style by fine-tuning the single dream blocks via LoRA, enabling the disentangled content to be re-embedded into new contexts. It includes two key components: (1) Rank-Constrained Adaptation. To preserve content identity and structure, we compress the rank and amplify the magnitude of updates within specific blocks, preventing content leakage into style blocks. (2) Visual-Gated LoRA. We split the content LoRA into two branches with different ranks, guided by image saliency. The high-rank branch preserves primary subject information, while the low-rank branch encodes residual details, mitigating content overfitting and enabling seamless re-embedding. Extensive experiments demonstrate that SplitFlux consistently outperforms state-of-the-art methods, achieving superior content preservation and stylization quality across diverse scenarios.
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            <a href="https://www.alphaxiv.org/abs/2511.15201v1" target="_blank" rel="noopener noreferrer">
                面向食品图像到食谱检索的无偏跨模态表示学习
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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            Towards Unbiased Cross-Modal Representation Learning for Food Image-to-Recipe Retrieval
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qing Wang, Chong-Wah Ngo, Ee-Peng Lim
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">虽然该论文涉及跨模态表示学习（类似于VLM处理异构数据的理念），但其特定领域是食品图像到食谱检索，这属于纯粹的视觉-语言应用，与推荐系统、搜索或广告没有明确的关联。食品检索是一个专门的垂直领域，缺乏在通用RecSys/Search/Ads系统中的直接应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 07:39:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15201v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15201v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.MM</span></div>
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                    This paper addresses the challenges of learning representations for recipes and food images in the cross-modal retrieval problem. As the relationship between a recipe and its cooked dish is cause-and-effect, treating a recipe as a text source describing the visual appearance of a dish for learning representation, as the existing approaches, will create bias misleading image-and-recipe similarity judgment. Specifically, a food image may not equally capture every detail in a recipe, due to factors such as the cooking process, dish presentation, and image-capturing conditions. The current representation learning tends to capture dominant visual-text alignment while overlooking subtle variations that determine retrieval relevance. In this paper, we model such bias in cross-modal representation learning using causal theory. The causal view of this problem suggests ingredients as one of the confounder sources and a simple backdoor adjustment can alleviate the bias. By causal intervention, we reformulate the conventional model for food-to-recipe retrieval with an additional term to remove the potential bias in similarity judgment. Based on this theory-informed formulation, we empirically prove the oracle performance of retrieval on the Recipe1M dataset to be MedR=1 across the testing data sizes of 1K, 10K, and even 50K. We also propose a plug-and-play neural module, which is essentially a multi-label ingredient classifier for debiasing. New state-of-the-art search performances are reported on the Recipe1M dataset.
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            <a href="https://www.alphaxiv.org/abs/2511.15197v1" target="_blank" rel="noopener noreferrer">
                插入风格：一种用于和谐跨域对象组合的零样本生成框架
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Insert In Style: A Zero-Shot Generative Framework for Harmonious Cross-Domain Object Composition
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Raghu Vamsi Chittersu, Yuvraj Singh Rathore, Pranav Adlinge, Kunal Swami
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉中的图像生成和对象组合任务，属于AIGC和内容生成领域。虽然标题提到了'跨域'概念，但这指的是视觉域之间的转换，而非推荐或搜索系统中的异构数据模态。该工作缺乏明确的推荐、搜索或广告应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 07:33:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15197v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15197v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Reference-based object composition methods fail when inserting real-world objects into stylized domains. This under-explored problem is currently split between practical "blenders" that lack generative fidelity and "generators" that require impractical, per-subject online finetuning. In this work, we introduce Insert In Style, the first zero-shot generative framework that is both practical and high-fidelity. Our core contribution is a unified framework with two key innovations: (i) a novel multi-stage training protocol that disentangles representations for identity, style, and composition, and (ii) a specialized masked-attention architecture that surgically enforces this disentanglement during generation. This approach prevents the concept interference common in general-purpose, unified-attention models. Our framework is trained on a new 100k sample dataset, curated from a novel data pipeline. This pipeline couples large-scale generation with a rigorous, two-stage filtering process to ensure both high-fidelity semantic identity and style coherence. Unlike prior work, our model is truly zero-shot and requires no text prompts. We also introduce a new public benchmark for stylized composition. We demonstrate state-of-the-art performance, significantly outperforming existing methods on both identity and style metrics, a result strongly corroborated by user studies.
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            <a href="https://www.alphaxiv.org/abs/2511.15179v1" target="_blank" rel="noopener noreferrer">
                MMCM：基于聚类模态的多模态感知度量方法，用于概率性人体运动预测
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            MMCM: Multimodality-aware Metric using Clustering-based Modes for Probabilistic Human Motion Prediction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kyotaro Tokoro, Hiromu Taketsugu, Norimichi Ukita
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的人体运动预测，虽然提到了多模态概念，但这是指视觉模态内的不同运动模式，而非RecSys/Search/Ads中处理异构数据（如用户序列和上下文特征）的统一建模。论文的核心应用场景是人体运动分析，与推荐系统、搜索或广告的排名和个性化需求缺乏直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 07:05:05
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15179v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15179v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    This paper proposes a novel metric for Human Motion Prediction (HMP). Since a single past sequence can lead to multiple possible futures, a probabilistic HMP method predicts such multiple motions. While a single motion predicted by a deterministic method is evaluated only with the difference from its ground truth motion, multiple predicted motions should also be evaluated based on their distribution. For this evaluation, this paper focuses on the following two criteria. \textbf{(a) Coverage}: motions should be distributed among multiple motion modes to cover diverse possibilities. \textbf{(b) Validity}: motions should be kinematically valid as future motions observable from a given past motion. However, existing metrics simply appreciate widely distributed motions even if these motions are observed in a single mode and kinematically invalid. To resolve these disadvantages, this paper proposes a Multimodality-aware Metric using Clustering-based Modes (MMCM). For (a) coverage, MMCM divides a motion space into several clusters, each of which is regarded as a mode. These modes are used to explicitly evaluate whether predicted motions are distributed among multiple modes. For (b) validity, MMCM identifies valid modes by collecting possible future motions from a motion dataset. Our experiments validate that our clustering yields sensible mode definitions and that MMCM accurately scores multimodal predictions. Code: https://github.com/placerkyo/MMCM
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            <a href="https://www.alphaxiv.org/abs/2511.15153v1" target="_blank" rel="noopener noreferrer">
                SceneEdited：基于图像引导变化检测的城市尺度3D高精地图更新基准
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            SceneEdited: A City-Scale Benchmark for 3D HD Map Updating via Image-Guided Change Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chun-Jung Lin, Tat-Jun Chin, Sourav Garg, Feras Dayoub
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D高精地图更新和计算机视觉中的变化检测，属于纯粹的视觉和地理空间领域。虽然地图数据可能间接支持位置感知推荐系统，但论文的核心技术（3D地图更新、图像引导变化检测）与推荐系统、搜索或广告的排名、建模或Transformer架构没有直接关联，也不涉及LLM技术或异构数据统一建模。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 06:10:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15153v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15153v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Accurate, up-to-date High-Definition (HD) maps are critical for urban planning, infrastructure monitoring, and autonomous navigation. However, these maps quickly become outdated as environments evolve, creating a need for robust methods that not only detect changes but also incorporate them into updated 3D representations. While change detection techniques have advanced significantly, there remains a clear gap between detecting changes and actually updating 3D maps, particularly when relying on 2D image-based change detection. To address this gap, we introduce SceneEdited, the first city-scale dataset explicitly designed to support research on HD map maintenance through 3D point cloud updating. SceneEdited contains over 800 up-to-date scenes covering 73 km of driving and approximate 3 $\text{km}^2$ of urban area, with more than 23,000 synthesized object changes created both manually and automatically across 2000+ out-of-date versions, simulating realistic urban modifications such as missing roadside infrastructure, buildings, overpasses, and utility poles. Each scene includes calibrated RGB images, LiDAR scans, and detailed change masks for training and evaluation. We also provide baseline methods using a foundational image-based structure-from-motion pipeline for updating outdated scenes, as well as a comprehensive toolkit supporting scalability, trackability, and portability for future dataset expansion and unification of out-of-date object annotations. Both the dataset and the toolkit are publicly available at https://github.com/ChadLin9596/ScenePoint-ETK, establising a standardized benchmark for 3D map updating research.
                </div>
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            <a href="https://www.alphaxiv.org/abs/2511.15118v1" target="_blank" rel="noopener noreferrer">
                基于视觉基础模型的少样本分割无偏语义解码
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Unbiased Semantic Decoding with Vision Foundation Models for Few-shot Segmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jin Wang, Bingfeng Zhang, Jian Pang, Weifeng Liu, Baodi Liu, Honglong Chen
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的少样本分割任务，虽然涉及基础模型概念，但其核心应用场景是视觉分割而非推荐系统、搜索或广告领域。论文提出的语义解码方法主要针对视觉模态，缺乏明确的跨模态应用潜力或与异构数据处理相关的创新，无法直接应用于RecSys/Search/Ads场景。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 04:41:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15118v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15118v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Few-shot segmentation has garnered significant attention. Many recent approaches attempt to introduce the Segment Anything Model (SAM) to handle this task. With the strong generalization ability and rich object-specific extraction ability of the SAM model, such a solution shows great potential in few-shot segmentation. However, the decoding process of SAM highly relies on accurate and explicit prompts, making previous approaches mainly focus on extracting prompts from the support set, which is insufficient to activate the generalization ability of SAM, and this design is easy to result in a biased decoding process when adapting to the unknown classes. In this work, we propose an Unbiased Semantic Decoding (USD) strategy integrated with SAM, which extracts target information from both the support and query set simultaneously to perform consistent predictions guided by the semantics of the Contrastive Language-Image Pre-training (CLIP) model. Specifically, to enhance the unbiased semantic discrimination of SAM, we design two feature enhancement strategies that leverage the semantic alignment capability of CLIP to enrich the original SAM features, mainly including a global supplement at the image level to provide a generalize category indicate with support image and a local guidance at the pixel level to provide a useful target location with query image. Besides, to generate target-focused prompt embeddings, a learnable visual-text target prompt generator is proposed by interacting target text embeddings and clip visual features. Without requiring re-training of the vision foundation models, the features with semantic discrimination draw attention to the target region through the guidance of prompt with rich target information.
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            <a href="https://www.alphaxiv.org/abs/2511.15092v1" target="_blank" rel="noopener noreferrer">
                用于多视角姿态引导人物图像合成的联合条件扩散模型
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Jointly Conditioned Diffusion Model for Multi-View Pose-Guided Person Image Synthesis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chengyu Xie, Zhi Gong, Junchi Ren, Linkun Yu, Si Shen, Fei Shen, Xiaoyu Du
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的人物图像生成，虽然使用了扩散模型技术，但主要应用于姿态引导的图像合成任务。这与推荐系统、搜索或广告的核心技术栈没有直接关联，也不涉及处理异构数据或多模态建模的通用方法。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 04:05:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15092v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15092v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Pose-guided human image generation is limited by incomplete textures from single reference views and the absence of explicit cross-view interaction. We present jointly conditioned diffusion model (JCDM), a jointly conditioned diffusion framework that exploits multi-view priors. The appearance prior module (APM) infers a holistic identity preserving prior from incomplete references, and the joint conditional injection (JCI) mechanism fuses multi-view cues and injects shared conditioning into the denoising backbone to align identity, color, and texture across poses. JCDM supports a variable number of reference views and integrates with standard diffusion backbones with minimal and targeted architectural modifications. Experiments demonstrate state of the art fidelity and cross-view consistency.
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            <a href="https://www.alphaxiv.org/abs/2511.15016v1" target="_blank" rel="noopener noreferrer">
                CKDA：面向可见光-红外终身行人重识别的跨模态知识解耦与对齐
            </a>
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        <div class="mb-2 text-base text-gray-700">
            CKDA: Cross-modality Knowledge Disentanglement and Alignment for Visible-Infrared Lifelong Person Re-identification
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhenyu Cui, Jiahuan Zhou, Yuxin Peng
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的跨模态行人重识别任务，虽然涉及跨模态学习概念，但其应用场景（可见光-红外行人重识别）与推荐系统、搜索或广告领域没有直接关联。论文提出的跨模态知识解耦与对齐方法主要针对视觉模态间的特征学习，缺乏在RecSys/Search/Ads领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 01:30:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15016v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15016v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Lifelong person Re-IDentification (LReID) aims to match the same person employing continuously collected individual data from different scenarios. To achieve continuous all-day person matching across day and night, Visible-Infrared Lifelong person Re-IDentification (VI-LReID) focuses on sequential training on data from visible and infrared modalities and pursues average performance over all data. To this end, existing methods typically exploit cross-modal knowledge distillation to alleviate the catastrophic forgetting of old knowledge. However, these methods ignore the mutual interference of modality-specific knowledge acquisition and modality-common knowledge anti-forgetting, where conflicting knowledge leads to collaborative forgetting. To address the above problems, this paper proposes a Cross-modality Knowledge Disentanglement and Alignment method, called CKDA, which explicitly separates and preserves modality-specific knowledge and modality-common knowledge in a balanced way. Specifically, a Modality-Common Prompting (MCP) module and a Modality-Specific Prompting (MSP) module are proposed to explicitly disentangle and purify discriminative information that coexists and is specific to different modalities, avoiding the mutual interference between both knowledge. In addition, a Cross-modal Knowledge Alignment (CKA) module is designed to further align the disentangled new knowledge with the old one in two mutually independent inter- and intra-modality feature spaces based on dual-modality prototypes in a balanced manner. Extensive experiments on four benchmark datasets verify the effectiveness and superiority of our CKDA against state-of-the-art methods. The source code of this paper is available at https://github.com/PKU-ICST-MIPL/CKDA-AAAI2026.
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            <a href="https://www.alphaxiv.org/abs/2511.15435v1" target="_blank" rel="noopener noreferrer">
                HV-Attack：面向多模态检索增强生成的分层视觉攻击
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        <div class="mb-2 text-base text-gray-700">
            HV-Attack: Hierarchical Visual Attack for Multimodal Retrieval Augmented Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Linyin Luo, Yujuan Ding, Yunshan Ma, Wenqi Fan, Hanjiang Lai
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究多模态检索增强生成中的视觉攻击方法，属于安全/对抗攻击领域，属于明确的无关主题。虽然涉及检索和生成，但核心关注点是攻击而非推荐/搜索系统本身的技术进展，也没有展示在推荐/搜索/广告领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 13:45:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15435v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15435v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.IR</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Advanced multimodal Retrieval-Augmented Generation (MRAG) techniques have been widely applied to enhance the capabilities of Large Multimodal Models (LMMs), but they also bring along novel safety issues. Existing adversarial research has revealed the vulnerability of MRAG systems to knowledge poisoning attacks, which fool the retriever into recalling injected poisoned contents. However, our work considers a different setting: visual attack of MRAG by solely adding imperceptible perturbations at the image inputs of users, without manipulating any other components. This is challenging due to the robustness of fine-tuned retrievers and large-scale generators, and the effect of visual perturbation may be further weakened by propagation through the RAG chain. We propose a novel Hierarchical Visual Attack that misaligns and disrupts the two inputs (the multimodal query and the augmented knowledge) of MRAG's generator to confuse its generation. We further design a hierarchical two-stage strategy to obtain misaligned augmented knowledge. We disrupt the image input of the retriever to make it recall irrelevant knowledge from the original database, by optimizing the perturbation which first breaks the cross-modal alignment and then disrupts the multimodal semantic alignment. We conduct extensive experiments on two widely-used MRAG datasets: OK-VQA and InfoSeek. We use CLIP-based retrievers and two LMMs BLIP-2 and LLaVA as generators. Results demonstrate the effectiveness of our visual attack on MRAG through the significant decrease in both retrieval and generation performance.
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            <a href="https://www.alphaxiv.org/abs/2511.15408v1" target="_blank" rel="noopener noreferrer">
                NAMeGEn：基于新型智能体多目标个性化增强框架的创意名称生成
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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            NAMeGEn: Creative Name Generation via A Novel Agent-based Multiple Personalized Goal Enhancement Framework
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shanlin Zhou, Xinpeng Wang, Jianxun Lian, Zhenghao Liu, Laks V. S. Lakshmanan, X...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于创意名称生成，这属于内容生成领域，与我的关注点中的推荐系统、搜索或广告排名无关。虽然提到了个性化，但核心是名称生成而非推荐或搜索任务，因此相关性极低。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 13:05:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15408v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15408v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.IR</span><span class="category-tag">cs.MA</span><span class="category-tag">cs.NE</span></div>
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                    Trained on diverse human-authored texts, Large Language Models (LLMs) unlocked the potential for Creative Natural Language Generation (CNLG), benefiting various applications like advertising and storytelling. Nevertheless, CNLG still remains difficult due to two main challenges. (1) Multi-objective flexibility: user requirements are often personalized, fine-grained, and pluralistic, which LLMs struggle to satisfy simultaneously; (2) Interpretive complexity: beyond generation, creativity also involves understanding and interpreting implicit meaning to enhance users' perception. These challenges significantly limit current methods, especially in short-form text generation, in generating creative and insightful content. To address this, we focus on Chinese baby naming, a representative short-form CNLG task requiring adherence to explicit user constraints (e.g., length, semantics, anthroponymy) while offering meaningful aesthetic explanations. We propose NAMeGEn, a novel multi-agent optimization framework that iteratively alternates between objective extraction, name generation, and evaluation to meet diverse requirements and generate accurate explanations. To support this task, we further construct a classical Chinese poetry corpus with 17k+ poems to enhance aesthetics, and introduce CBNames, a new benchmark with tailored metrics. Extensive experiments demonstrate that NAMeGEn effectively generates creative names that meet diverse, personalized requirements while providing meaningful explanations, outperforming six baseline methods spanning various LLM backbones without any training.
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            <a href="https://www.alphaxiv.org/abs/2511.15061v1" target="_blank" rel="noopener noreferrer">
                超越GeneGPT：基于开源大语言模型的多智能体架构，用于增强基因组问答
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            Beyond GeneGPT: A Multi-Agent Architecture with Open-Source LLMs for Enhanced Genomic Question Answering
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haodong Chen, Guido Zuccon, Teerapong Leelanupab
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于基因组问答的特定生物学应用，属于明确的无关领域。论文标题明确指向基因组学和生物学问答，与推荐系统、搜索或广告的核心技术领域没有任何关联。即使涉及多智能体架构和LLM技术，其应用场景完全局限于生物医学领域，不符合任何关注点要求。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 03:08:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15061v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15061v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.IR</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Genomic question answering often requires complex reasoning and integration across diverse biomedical sources. GeneGPT addressed this challenge by combining domain-specific APIs with OpenAI's code-davinci-002 large language model to enable natural language interaction with genomic databases. However, its reliance on a proprietary model limits scalability, increases operational costs, and raises concerns about data privacy and generalization. In this work, we revisit and reproduce GeneGPT in a pilot study using open source models, including Llama 3.1, Qwen2.5, and Qwen2.5 Coder, within a monolithic architecture; this allows us to identify the limitations of this approach. Building on this foundation, we then develop OpenBioLLM, a modular multi-agent framework that extends GeneGPT by introducing agent specialization for tool routing, query generation, and response validation. This enables coordinated reasoning and role-based task execution. OpenBioLLM matches or outperforms GeneGPT on over 90% of the benchmark tasks, achieving average scores of 0.849 on Gene-Turing and 0.830 on GeneHop, while using smaller open-source models without additional fine-tuning or tool-specific pretraining. OpenBioLLM's modular multi-agent design reduces latency by 40-50% across benchmark tasks, significantly improving efficiency without compromising model capability. The results of our comprehensive evaluation highlight the potential of open-source multi-agent systems for genomic question answering. Code and resources are available at https://github.com/ielab/OpenBioLLM.
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            <a href="https://www.alphaxiv.org/abs/2511.15574v1" target="_blank" rel="noopener noreferrer">
                HSK基准：通过课程调优在大型语言模型中建模和基准测试汉语作为第二语言习得
            </a>
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            HSKBenchmark: Modeling and Benchmarking Chinese Second Language Acquisition in Large Language Models through Curriculum Tuning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qihao Yang, Xuelin Wang, Jiale Chen, Xuelian Dong, Yuxin Hao, Tianyong Hao
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于语言习得基准测试和课程调优，属于纯教育语言学应用领域。与搜索、推荐或广告系统没有任何技术关联，也不涉及核心LLM技术进展、Transformer架构改进或异构数据建模。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 16:06:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15574v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15574v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Language acquisition is vital to revealing the nature of human language intelligence and has recently emerged as a promising perspective for improving the interpretability of large language models (LLMs). However, it is ethically and practically infeasible to conduct experiments that require controlling human learners' language inputs. This poses challenges for the verifiability and scalability of language acquisition modeling, particularly in Chinese second language acquisition (SLA). While LLMs provide a controllable and reproducible alternative, a systematic benchmark to support phase-wise modeling and assessment is still lacking. In this paper, we present HSKBenchmark, the first benchmark for staged modeling and writing assessment of LLMs in Chinese SLA. It covers HSK levels 3 to 6 and includes authentic textbooks with 6.76 million tokens, 16K synthetic instruction samples, 30 test topics, and a linguistically grounded evaluation system. To simulate human learning trajectories, we introduce a curriculum-tuning framework that trains models from beginner to advanced levels. An evaluation system is created to examine level-based grammar coverage, writing errors, lexical and syntactic complexity, and holistic scoring. We also build HSKAgent, fine-tuned on 10K learner compositions. Extensive experimental results demonstrate that HSKBenchmark not only models Chinese SLA effectively, but also serves as a reliable benchmark for dynamic writing assessment in LLMs. Our fine-tuned LLMs have writing performance on par with advanced human learners and exhibit human-like acquisition characteristics. The HSKBenchmark, HSKAgent, and checkpoints serve as foundational tools and resources, with the potential to pave the way for future research on language acquisition modeling and LLMs interpretability. Code and data are publicly available at: https://github.com/CharlesYang030/HSKB.
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            <a href="https://www.alphaxiv.org/abs/2511.15418v1" target="_blank" rel="noopener noreferrer">
                为印度语言构建鲁棒且可扩展的多语言自动语音识别系统
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            Building Robust and Scalable Multilingual ASR for Indian Languages
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Arjun Gangwar, Kaousheik Jayakumar, S. Umesh
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于多语言语音识别技术，属于语音处理领域，与搜索、推荐或广告系统的核心需求没有直接关联。虽然语音搜索是搜索系统的一个潜在应用场景，但该论文主要解决印度语言的ASR技术挑战，而非搜索/推荐/广告系统的核心算法或架构创新。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 13:17:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15418v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15418v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    This paper describes the systems developed by SPRING Lab, Indian Institute of Technology Madras, for the ASRU MADASR 2.0 challenge. The systems developed focuses on adapting ASR systems to improve in predicting the language and dialect of the utterance among 8 languages across 33 dialects. We participated in Track 1 and Track 2, which restricts the use of additional data and develop from-the-scratch multilingual systems. We presented a novel training approach using Multi-Decoder architecture with phonemic Common Label Set (CLS) as intermediate representation. It improved the performance over the baseline (in the CLS space). We also discuss various methods used to retain the gain obtained in the phonemic space while converting them back to the corresponding grapheme representations. Our systems beat the baseline in 3 languages (Track 2) in terms of WER/CER and achieved the highest language ID and dialect ID accuracy among all participating teams (Track 2).
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            <a href="https://www.alphaxiv.org/abs/2511.15355v1" target="_blank" rel="noopener noreferrer">
                HEAD-QA v2：扩展医疗推理基准
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            HEAD-QA v2: Expanding a Healthcare Benchmark for Reasoning
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Alexis Correa-Guillén, Carlos Gómez-Rodríguez, David Vilares
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文明确聚焦于医疗领域的基准测试扩展，属于明确的医疗应用范畴，在无关主题中被明确排除。标题中提到的推理基准虽然涉及LLM能力，但完全限定在医疗领域，与推荐系统、搜索或广告没有任何直接或间接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 11:31:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15355v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15355v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    We introduce HEAD-QA v2, an expanded and updated version of a Spanish/English healthcare multiple-choice reasoning dataset originally released by Vilares and Gómez-Rodríguez (2019). The update responds to the growing need for high-quality datasets that capture the linguistic and conceptual complexity of healthcare reasoning. We extend the dataset to over 12,000 questions from ten years of Spanish professional exams, benchmark several open-source LLMs using prompting, RAG, and probability-based answer selection, and provide additional multilingual versions to support future work. Results indicate that performance is mainly driven by model scale and intrinsic reasoning ability, with complex inference strategies obtaining limited gains. Together, these results establish HEAD-QA v2 as a reliable resource for advancing research on biomedical reasoning and model improvement.
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            <a href="https://www.alphaxiv.org/abs/2511.15323v1" target="_blank" rel="noopener noreferrer">
                SkyEgg：使用E-图进行硬件综合的联合实现选择与调度
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            SkyEgg: Joint Implementation Selection and Scheduling for Hardware Synthesis using E-graphs
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Youwei Xiao, Yuyang Zou, Yun Liang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于硬件综合和编译器优化技术，属于计算机体系结构和EDA领域。虽然E-图技术本身是通用的优化方法，但论文标题明确指向硬件合成和调度问题，与推荐系统、搜索、广告或相关LLM技术没有直接关联。该工作主要服务于芯片设计和硬件加速器开发，而非用户建模、内容排名或Transformer架构改进。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 10:39:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15323v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15323v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.PL</span><span class="category-tag">cs.CL</span></div>
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                    Hardware synthesis from high-level descriptions remains fundamentally limited by the sequential optimization of interdependent design decisions. Current methodologies, including state-of-the-art high-level synthesis (HLS) tools, artificially separate implementation selection from scheduling, leading to suboptimal designs that cannot fully exploit modern FPGA heterogeneous architectures. Implementation selection is typically performed by ad-hoc pattern matching on operations, a process that does not consider the impact on scheduling. Subsequently, scheduling algorithms operate on fixed selection solutions with inaccurate delay estimates, which misses critical optimization opportunities from appropriately configured FPGA blocks like DSP slices. We present SkyEgg, a novel hardware synthesis framework that jointly optimizes implementation selection and scheduling using the e-graph data structure. Our key insight is that both algebraic transformations and hardware implementation choices can be uniformly represented as rewrite rules within an e-graph, modeling the complete design space of implementation candidates to be selected and scheduled together. First, SkyEgg constructs an e-graph from the input program. It then applies both algebraic and implementation rewrites through equality saturation. Finally, it formulates the joint optimization as a mixed-integer linear programming (MILP) problem on the saturated e-graph. We provide both exact MILP solving and an efficient ASAP heuristic for scalable synthesis. Our evaluation on benchmarks from diverse applications targeting Xilinx Kintex UltraScale+ FPGAs demonstrates that SkyEgg achieves an average speedup of 3.01x over Vitis HLS, with improvements up to 5.22x for complex expressions.
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            <a href="https://www.alphaxiv.org/abs/2511.15304v1" target="_blank" rel="noopener noreferrer">
                对抗性诗歌作为大型语言模型中的通用单轮越狱机制
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            Adversarial Poetry as a Universal Single-Turn Jailbreak Mechanism in Large Language Models
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Piercosma Bisconti, Matteo Prandi, Federico Pierucci, Francesco Giarrusso, Marca...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注LLM的安全漏洞和对抗性攻击（越狱机制），这属于安全相关主题，被明确列为不相关范畴。论文内容涉及模型安全性和对抗性攻击，与推荐系统、搜索或广告的核心技术进展、LLM应用或Transformer架构改进没有直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 10:14:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15304v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15304v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    We present evidence that adversarial poetry functions as a universal single-turn jailbreak technique for large language models (LLMs). Across 25 frontier proprietary and open-weight models, curated poetic prompts yielded high attack-success rates (ASR), with some providers exceeding 90%. Mapping prompts to MLCommons and EU CoP risk taxonomies shows that poetic attacks transfer across CBRN, manipulation, cyber-offence, and loss-of-control domains. Converting 1,200 MLCommons harmful prompts into verse via a standardized meta-prompt produced ASRs up to 18 times higher than their prose baselines. Outputs are evaluated using an ensemble of open-weight judge models and a human-validated stratified subset (with double-annotations to measure agreement). Disagreements were manually resolved. Poetic framing achieved an average jailbreak success rate of 62% for hand-crafted poems and approximately 43% for meta-prompt conversions (compared to non-poetic baselines), substantially outperforming non-poetic baselines and revealing a systematic vulnerability across model families and safety training approaches. These findings demonstrate that stylistic variation alone can circumvent contemporary safety mechanisms, suggesting fundamental limitations in current alignment methods and evaluation protocols.
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                ChartEditor：一个基于强化学习的鲁棒图表编辑框架
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        <div class="mb-2 text-base text-gray-700">
            ChartEditor: A Reinforcement Learning Framework for Robust Chart Editing
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Liangyu Chen, Yichen Xu, Jianzhe Ma, Yuqi Liu, Donglu Yang, Liang Zhang, Wenxuan...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于图表编辑任务，属于内容生成和交互式编辑领域，与推荐系统、搜索或广告的核心排名和建模问题无关。强化学习框架应用于图表编辑，没有明确的RecSys/Search/Ads应用潜力，属于纯粹的AIGC/内容生成范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 09:27:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15266v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15266v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.MM</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Chart editing reduces manual effort in visualization design. Typical benchmarks limited in data diversity and assume access to complete chart code, which is seldom in real-world scenarios. To address this gap, we present ChartEditVista, a comprehensive benchmark consisting of 7,964 samples spanning 31 chart categories. It encompasses diverse editing instructions and covers nearly all editable chart elements. The inputs in ChartEditVista include only the original chart image and natural language editing instructions, without the original chart codes. ChartEditVista is generated through a fully automated pipeline that produces, edits, and verifies charts, ensuring high-quality chart editing data. Besides, we introduce two novel fine-grained, rule-based evaluation metrics: the layout metric, which evaluates the position, size and color of graphical components; and the text metric, which jointly assesses textual content and font styling. Building on top of ChartEditVista, we present ChartEditor, a model trained using a reinforcement learning framework that incorporates a novel rendering reward to simultaneously enforce code executability and visual fidelity. Through extensive experiments and human evaluations, we demonstrate that ChartEditVista provides a robust evaluation, while ChartEditor consistently outperforms models with similar-scale and larger-scale on chart editing tasks.
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            <a href="https://www.alphaxiv.org/abs/2511.15260v1" target="_blank" rel="noopener noreferrer">
                IndicGEC：强大模型，还是测量幻象？
            </a>
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            IndicGEC: Powerful Models, or a Measurement Mirage?
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sowmya Vajjala
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明其关注印度语言语法错误纠正（GEC）的模型评估问题，这属于纯粹的NLP评估基准研究。该主题与推荐系统、搜索或广告的核心技术进展无关，也不涉及能够应用于这些领域的LLM技术、Transformer架构改进或异构数据建模方法。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 09:24:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15260v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15260v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    In this paper, we report the results of the TeamNRC's participation in the BHASHA-Task 1 Grammatical Error Correction shared task https://github.com/BHASHA-Workshop/IndicGEC2025/ for 5 Indian languages. Our approach, focusing on zero/few-shot prompting of language models of varying sizes (4B to large proprietary models) achieved a Rank 4 in Telugu and Rank 2 in Hindi with GLEU scores of 83.78 and 84.31 respectively. In this paper, we extend the experiments to the other three languages of the shared task - Tamil, Malayalam and Bangla, and take a closer look at the data quality and evaluation metric used. Our results primarily highlight the potential of small language models, and summarize the concerns related to creating good quality datasets and appropriate metrics for this task that are suitable for Indian language scripts.
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            <a href="https://www.alphaxiv.org/abs/2511.15211v1" target="_blank" rel="noopener noreferrer">
                OEMA：面向零样本临床命名实体识别的本体增强多智能体协作框架
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            OEMA: Ontology-Enhanced Multi-Agent Collaboration Framework for Zero-Shot Clinical Named Entity Recognition
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xinli Tao, Xin Dong, Xuezhong Zhou
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于临床命名实体识别这一医学领域的特定应用，属于明确的医疗领域研究。虽然涉及多智能体协作和零样本学习技术，但这些技术在该论文中完全应用于医疗文本处理，与推荐系统、搜索或广告领域没有任何关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 08:02:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15211v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15211v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Clinical named entity recognition (NER) is crucial for extracting information from electronic health records (EHRs), but supervised models like CRF and BioClinicalBERT require costly annotated data. While zero-shot NER with large language models (LLMs) reduces this dependency, it struggles with example selection granularity and integrating prompts with self-improvement. To address this, we propose OEMA, a zero-shot clinical NER framework using multi-agent collaboration. OEMA's three components are: a self-annotator generating examples, a discriminator filtering them via SNOMED CT, and a predictor using entity descriptions for accurate inference. On MTSamples and VAERS datasets, OEMA achieves state-of-the-art exact-match performance. Under related-match, it matches supervised BioClinicalBERT and surpasses CRF. OEMA addresses key zero-shot NER challenges through ontology-guided reasoning and multi-agent collaboration, achieving near-supervised performance and showing promise for clinical NLP applications.
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            <a href="https://www.alphaxiv.org/abs/2511.15183v1" target="_blank" rel="noopener noreferrer">
                HinTel-AlignBench：一个包含英语对齐样本的印地语-泰卢固语框架与基准
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            HinTel-AlignBench: A Framework and Benchmark for Hindi-Telugu with English-Aligned Samples
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Rishikant Chigrupaatii, Ponnada Sai Tulasi Kanishka, Lalit Chandra Routhu, Marti...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于多语言基准测试和语言对齐，属于纯粹的NLP评估基准范畴。虽然多语言能力在理论上可能对某些搜索系统有用，但该工作没有展示与推荐系统、搜索排名或广告相关的具体技术进展或应用潜力，完全属于被排除的评估基准类别。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 07:11:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15183v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15183v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    With nearly 1.5 billion people and more than 120 major languages, India represents one of the most diverse regions in the world. As multilingual Vision-Language Models (VLMs) gain prominence, robust evaluation methodologies are essential to drive progress toward equitable AI for low-resource languages. Current multilingual VLM evaluations suffer from four major limitations: reliance on unverified auto-translations, narrow task/domain coverage, limited sample sizes, and lack of cultural and natively sourced Question-Answering (QA). To address these gaps, we present a scalable framework to evaluate VLMs in Indian languages and compare it with performance in English. Using the framework, we generate HinTel-AlignBench, a benchmark that draws from diverse sources in Hindi and Telugu with English-aligned samples. Our contributions are threefold: (1) a semi-automated dataset creation framework combining back-translation, filtering, and human verification; (2) the most comprehensive vision-language benchmark for Hindi and and Telugu, including adapted English datasets (VQAv2, RealWorldQA, CLEVR-Math) and native novel Indic datasets (JEE for STEM, VAANI for cultural grounding) with approximately 4,000 QA pairs per language; and (3) a detailed performance analysis of various State-of-the-Art (SOTA) open-weight and closed-source VLMs. We find a regression in performance for tasks in English versus in Indian languages for 4 out of 5 tasks across all the models, with an average regression of 8.3 points in Hindi and 5.5 points for Telugu. We categorize common failure modes to highlight concrete areas of improvement in multilingual multimodal understanding.
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            <a href="https://www.alphaxiv.org/abs/2511.15159v1" target="_blank" rel="noopener noreferrer">
                生成自然语言手术反馈：从结构化表示到领域接地评估
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            Generating Natural-Language Surgical Feedback: From Structured Representation to Domain-Grounded Evaluation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Firdavs Nasriddinov, Rafal Kocielnik, Anima Anandkumar, Andrew J. Hung
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的手术反馈生成，属于医疗应用范畴，与推荐系统、搜索或广告完全无关。论文内容涉及医疗领域的自然语言生成和评估，不在当前关注的技术范围内。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 06:19:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15159v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15159v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                    High-quality intraoperative feedback from a surgical trainer is pivotal for improving trainee performance and long-term skill acquisition. Automating natural, trainer-style feedback promises timely, accessible, and consistent guidance at scale but requires models that understand clinically relevant representations. We present a structure-aware pipeline that learns a surgical action ontology from real trainer-to-trainee transcripts (33 surgeries) and uses it to condition feedback generation. We contribute by (1) mining Instrument-Action-Target (IAT) triplets from real-world feedback text and clustering surface forms into normalized categories, (2) fine-tuning a video-to-IAT model that leverages the surgical procedure and task contexts as well as fine-grained temporal instrument motion, and (3) demonstrating how to effectively use IAT triplet representations to guide GPT-4o in generating clinically grounded, trainer-style feedback. We show that, on Task 1: Video-to-IAT recognition, our context injection and temporal tracking deliver consistent AUC gains (Instrument: 0.67 to 0.74; Action: 0.60 to 0.63; Tissue: 0.74 to 0.79). For Task 2: feedback text generation (rated on a 1-5 fidelity rubric where 1 = opposite/unsafe, 3 = admissible, and 5 = perfect match to a human trainer), GPT-4o from video alone scores 2.17, while IAT conditioning reaches 2.44 (+12.4%), doubling the share of admissible generations with score >= 3 from 21% to 42%. Traditional text-similarity metrics also improve: word error rate decreases by 15-31% and ROUGE (phrase/substring overlap) increases by 9-64%. Grounding generation in explicit IAT structure improves fidelity and yields clinician-verifiable rationales, supporting auditable use in surgical training.
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            <a href="https://www.alphaxiv.org/abs/2511.15131v1" target="_blank" rel="noopener noreferrer">
                CASTELLA：带有时序边界标注的长音频数据集
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            CASTELLA: Long Audio Dataset with Captions and Temporal Boundaries
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hokuto Munakata, Takehiro Imamura, Taichi Nishimura, Tatsuya Komatsu
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于音频数据集构建，属于纯语音领域，与推荐系统、搜索或广告的核心技术无直接关联。即使考虑多模态应用，音频数据在RecSys/Search/Ads中的实际应用场景非常有限，且论文未提及任何与推荐、搜索或广告相关的技术要素。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 05:19:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15131v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15131v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">eess.AS</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.SD</span></div>
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                    We introduce CASTELLA, a human-annotated audio benchmark for the task of audio moment retrieval (AMR). Although AMR has various useful potential applications, there is still no established benchmark with real-world data. The early study of AMR trained the model with solely synthetic datasets. Moreover, the evaluation is based on annotated dataset of fewer than 100 samples. This resulted in less reliable reported performance. To ensure performance for applications in real-world environments, we present CASTELLA, a large-scale manually annotated AMR dataset. CASTELLA consists of 1,009, 213, and 640 audio recordings for train, valid, and test split, respectively, which is 24 times larger than the previous dataset. We also establish a baseline model for AMR using CASTELLA. Our experiments demonstrate that a model fine-tuned on CASTELLA after pre-training on the synthetic data outperformed a model trained solely on the synthetic data by 10.4 points in Recall1@0.7. CASTELLA is publicly available in https://h-munakata.github.io/CASTELLA-demo/.
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            <a href="https://www.alphaxiv.org/abs/2511.15005v1" target="_blank" rel="noopener noreferrer">
                大型语言模型幻觉动力学的数学分析：不确定性量化、高级解码与原则性缓解
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            Mathematical Analysis of Hallucination Dynamics in Large Language Models: Uncertainty Quantification, Advanced Decoding, and Principled Mitigation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Moses Kiprono
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM的幻觉问题、不确定性量化和缓解策略，这属于纯粹的NLP评估和可靠性问题。根据用户明确的排除标准，'Hallucination, Evaluation benchmarks, or other purely NLP-centric topics' 被列为不相关主题，且论文标题未显示任何与推荐系统、搜索或广告的直接或潜在应用关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 00:58:36
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                <a href="https://arxiv.org/abs/2511.15005v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15005v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Large Language Models (LLMs) are powerful linguistic engines but remain susceptible to hallucinations: plausible-sounding outputs that are factually incorrect or unsupported. In this work, we present a mathematically grounded framework to understand, measure, and mitigate these hallucinations. Drawing on probabilistic modeling, information theory, trigonometric signal analysis, and Bayesian uncertainty estimation, we analyze how errors compound autoregressively, propose refined uncertainty metrics, including semantic and phase-aware variants, and develop principled mitigation strategies such as contrastive decoding, retrieval-augmented grounding, factual alignment, and abstention. This unified lens connects recent advances in calibration, retrieval, and alignment to support safer and more reliable LLMs.
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            <a href="https://www.alphaxiv.org/abs/2511.15704v1" target="_blank" rel="noopener noreferrer">
                In-N-On：利用真实世界数据和任务内数据扩展以自我为中心的操纵
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            In-N-On: Scaling Egocentric Manipulation with in-the-wild and on-task Data
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiongyi Cai, Ri-Zhao Qiu, Geng Chen, Lai Wei, Isabella Liu, Tianshu Huang, Xuxin...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于机器人操纵和真实世界数据收集，属于机器人技术领域。虽然提到了数据扩展，但内容与推荐系统、搜索、广告或Transformer架构无关，也没有任何明显的潜在应用可以关联到我的关注领域。这是一个纯粹的机器人研究主题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 18:59:04
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                <a href="https://arxiv.org/abs/2511.15704v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15704v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                    Egocentric videos are a valuable and scalable data source to learn manipulation policies. However, due to significant data heterogeneity, most existing approaches utilize human data for simple pre-training, which does not unlock its full potential. This paper first provides a scalable recipe for collecting and using egocentric data by categorizing human data into two categories: in-the-wild and on-task alongside with systematic analysis on how to use the data. We first curate a dataset, PHSD, which contains over 1,000 hours of diverse in-the-wild egocentric data and over 20 hours of on-task data directly aligned to the target manipulation tasks. This enables learning a large egocentric language-conditioned flow matching policy, Human0. With domain adaptation techniques, Human0 minimizes the gap between humans and humanoids. Empirically, we show Human0 achieves several novel properties from scaling human data, including language following of instructions from only human data, few-shot learning, and improved robustness using on-task data. Project website: https://xiongyicai.github.io/In-N-On/
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            <a href="https://www.alphaxiv.org/abs/2511.15692v1" target="_blank" rel="noopener noreferrer">
                基于光谱-空间混合器网络的高光谱图像分类
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            Hyperspectral Image Classification using Spectral-Spatial Mixer Network
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mohammed Q. Alkhatib
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于高光谱图像分类，属于纯粹的计算机视觉领域，与推荐系统、搜索或广告没有直接关联。虽然混合器网络架构可能在某些方面与Transformer相关，但该研究没有展示在异构数据处理或推荐系统应用方面的潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 18:48:52
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                <a href="https://arxiv.org/abs/2511.15692v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15692v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    This paper introduces SS-MixNet, a lightweight and effective deep learning model for hyperspectral image (HSI) classification. The architecture integrates 3D convolutional layers for local spectral-spatial feature extraction with two parallel MLP-style mixer blocks that capture long-range dependencies in spectral and spatial dimensions. A depthwise convolution-based attention mechanism is employed to enhance discriminative capability with minimal computational overhead. The model is evaluated on the QUH-Tangdaowan and QUH-Qingyun datasets using only 1% of labeled data for training and validation. SS-MixNet achieves the highest performance among compared methods, including 2D-CNN, 3D-CNN, IP-SWIN, SimPoolFormer, and HybridKAN, reaching 95.68% and 93.86% overall accuracy on the Tangdaowan and Qingyun datasets, respectively. The results, supported by quantitative metrics and classification maps, confirm the model's effectiveness in delivering accurate and robust predictions with limited supervision. The code will be made publicly available at: https://github.com/mqalkhatib/SS-MixNet
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            <a href="https://www.alphaxiv.org/abs/2511.15675v1" target="_blank" rel="noopener noreferrer">
                MF-GCN：一种基于眼动追踪、面部和声学特征的三模态抑郁症检测的多频图卷积网络
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            MF-GCN: A Multi-Frequency Graph Convolutional Network for Tri-Modal Depression Detection Using Eye-Tracking, Facial, and Acoustic Features
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sejuti Rahman, Swakshar Deb, MD. Sameer Iqbal Chowdhury, MD. Jubair Ahmed Sourov...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于使用多模态数据（眼动、面部、声学）进行抑郁症检测，这属于医疗/生物医学应用领域。虽然涉及多模态建模，但其应用场景（抑郁症诊断）与推荐系统、搜索或广告完全无关，且不涉及任何LLM、Transformer或推荐相关技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 18:18:53
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                <a href="https://arxiv.org/abs/2511.15675v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15675v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Eye tracking data quantifies the attentional bias towards negative stimuli that is frequently observed in depressed groups. Audio and video data capture the affective flattening and psychomotor retardation characteristic of depression. Statistical validation confirmed their significant discriminative power in distinguishing depressed from non depressed groups. We address a critical limitation of existing graph-based models that focus on low-frequency information and propose a Multi-Frequency Graph Convolutional Network (MF-GCN). This framework consists of a novel Multi-Frequency Filter Bank Module (MFFBM), which can leverage both low and high frequency signals. Extensive evaluation against traditional machine learning algorithms and deep learning frameworks demonstrates that MF-GCN consistently outperforms baselines. In binary (depressed and non depressed) classification, the model achieved a sensitivity of 0.96 and F2 score of 0.94. For the 3 class (no depression, mild to moderate depression and severe depression) classification task, the proposed method achieved a sensitivity of 0.79 and specificity of 0.87 and siginificantly suprassed other models. To validate generalizability, the model was also evaluated on the Chinese Multimodal Depression Corpus (CMDC) dataset and achieved a sensitivity of 0.95 and F2 score of 0.96. These results confirm that our trimodal, multi frequency framework effectively captures cross modal interaction for accurate depression detection.
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            <a href="https://www.alphaxiv.org/abs/2511.15658v1" target="_blank" rel="noopener noreferrer">
                GEO-Bench-2：从性能到能力，重新思考地理空间人工智能中的评估
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            GEO-Bench-2: From Performance to Capability, Rethinking Evaluation in Geospatial AI
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Naomi Simumba, Nils Lehmann, Paolo Fraccaro, Hamed Alemohammad, Geeth De Mel, Sa...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于地理空间AI的评估基准，属于高度特定领域（地理空间）的应用，与推荐系统、搜索或广告的核心领域无关。虽然涉及评估方法，但这是针对地理空间领域的专门评估，没有显示出在推荐/搜索/广告领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 17:45:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15658v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15658v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Geospatial Foundation Models (GeoFMs) are transforming Earth Observation (EO), but evaluation lacks standardized protocols. GEO-Bench-2 addresses this with a comprehensive framework spanning classification, segmentation, regression, object detection, and instance segmentation across 19 permissively-licensed datasets. We introduce ''capability'' groups to rank models on datasets that share common characteristics (e.g., resolution, bands, temporality). This enables users to identify which models excel in each capability and determine which areas need improvement in future work. To support both fair comparison and methodological innovation, we define a prescriptive yet flexible evaluation protocol. This not only ensures consistency in benchmarking but also facilitates research into model adaptation strategies, a key and open challenge in advancing GeoFMs for downstream tasks. Our experiments show that no single model dominates across all tasks, confirming the specificity of the choices made during architecture design and pretraining. While models pretrained on natural images (ConvNext ImageNet, DINO V3) excel on high-resolution tasks, EO-specific models (TerraMind, Prithvi, and Clay) outperform them on multispectral applications such as agriculture and disaster response. These findings demonstrate that optimal model choice depends on task requirements, data modalities, and constraints. This shows that the goal of a single GeoFM model that performs well across all tasks remains open for future research. GEO-Bench-2 enables informed, reproducible GeoFM evaluation tailored to specific use cases. Code, data, and leaderboard for GEO-Bench-2 are publicly released under a permissive license.
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                MambaIO：基于多尺度频率解耦建模的行人全局坐标惯性里程计
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            MambaIO: Global-Coordinate Inertial Odometry for Pedestrians via Multi-Scale Frequency-Decoupled Modeling
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shanshan Zhang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于惯性里程计和行人定位技术，属于计算机视觉和机器人领域的定位导航问题。论文内容涉及多尺度频率解耦建模，但主要应用于物理运动轨迹估计，与推荐系统、搜索或广告的核心技术领域没有直接关联，也没有明显的Transformer架构或LLM技术应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 17:29:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15645v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15645v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span></div>
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                    Inertial Odometry (IO) enables real-time localization using only acceleration and angular velocity measurements from an Inertial Measurement Unit (IMU), making it a promising solution for localization in consumer-grade applications. Traditionally, IMU measurements in IO have been processed under two coordinate system paradigms: the body coordinate frame and the global coordinate frame, with the latter being widely adopted. However, recent studies in drone scenarios have demonstrated that the body frame can significantly improve localization accuracy, prompting a re-evaluation of the suitability of the global frame for pedestrian IO. To address this issue, this paper systematically evaluates the effectiveness of the global coordinate frame in pedestrian IO through theoretical analysis, qualitative inspection, and quantitative experiments. Building upon these findings, we further propose MambaIO, which decomposes IMU measurements into high-frequency and low-frequency components using a Laplacian pyramid. The low-frequency component is processed by a Mamba architecture to extract implicit contextual motion cues, while the high-frequency component is handled by a convolutional structure to capture fine-grained local motion details. Experiments on multiple public datasets show that MambaIO substantially reduces localization error and achieves state-of-the-art (SOTA) performance. To the best of our knowledge, this is the first application of the Mamba architecture to the inertial odometry task.
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            <a href="https://www.alphaxiv.org/abs/2511.15640v1" target="_blank" rel="noopener noreferrer">
                用于一致性超声应变弹性成像的多阶段残差感知无监督深度学习框架
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            Multi-Stage Residual-Aware Unsupervised Deep Learning Framework for Consistent Ultrasound Strain Elastography
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shourov Joarder, Tushar Talukder Showrav, Md. Kamrul Hasan
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学超声弹性成像领域，属于医学影像处理的特定应用。标题中提到的多阶段残差感知框架和无监督深度学习都是医学影像分析的技术，与推荐系统、搜索或广告领域没有任何直接或间接关联。该研究完全属于被排除的医学领域应用范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 17:22:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15640v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15640v1
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                    Ultrasound Strain Elastography (USE) is a powerful non-invasive imaging technique for assessing tissue mechanical properties, offering crucial diagnostic value across diverse clinical applications. However, its clinical application remains limited by tissue decorrelation noise, scarcity of ground truth, and inconsistent strain estimation under different deformation conditions. Overcoming these barriers, we propose MUSSE-Net, a residual-aware, multi-stage unsupervised sequential deep learning framework designed for robust and consistent strain estimation. At its backbone lies our proposed USSE-Net, an end-to-end multi-stream encoder-decoder architecture that parallelly processes pre- and post-deformation RF sequences to estimate displacement fields and axial strains. The novel architecture incorporates Context-Aware Complementary Feature Fusion (CACFF)-based encoder with Tri-Cross Attention (TCA) bottleneck with a Cross-Attentive Fusion (CAF)-based sequential decoder. To ensure temporal coherence and strain stability across varying deformation levels, this architecture leverages a tailored consistency loss. Finally, with the MUSSE-Net framework, a secondary residual refinement stage further enhances accuracy and suppresses noise. Extensive validation on simulation, in vivo, and private clinical datasets from Bangladesh University of Engineering and Technology (BUET) medical center, demonstrates MUSSE-Net's outperformed existing unsupervised approaches. On MUSSE-Net achieves state-of-the-art performance with a target SNR of 24.54, background SNR of 132.76, CNR of 59.81, and elastographic SNR of 9.73 on simulation data. In particular, on the BUET dataset, MUSSE-Net produces strain maps with enhanced lesion-to-background contrast and significant noise suppression yielding clinically interpretable strain patterns.
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            <a href="https://www.alphaxiv.org/abs/2511.15622v1" target="_blank" rel="noopener noreferrer">
                SA-FARI数据集：用于动物识别与身份鉴定的任意片段动物影像数据集
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            The SA-FARI Dataset: Segment Anything in Footage of Animals for Recognition and Identification
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dante Francisco Wasmuht, Otto Brookes, Maximillian Schall, Pablo Palencia, Chris...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于动物识别领域的计算机视觉数据集构建，与推荐系统、搜索或广告的核心技术领域完全无关。虽然涉及识别技术，但属于纯粹的视觉应用场景，没有任何与推荐、搜索或广告相关的技术交叉点或潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 17:07:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15622v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15622v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Automated video analysis is critical for wildlife conservation. A foundational task in this domain is multi-animal tracking (MAT), which underpins applications such as individual re-identification and behavior recognition. However, existing datasets are limited in scale, constrained to a few species, or lack sufficient temporal and geographical diversity - leaving no suitable benchmark for training general-purpose MAT models applicable across wild animal populations. To address this, we introduce SA-FARI, the largest open-source MAT dataset for wild animals. It comprises 11,609 camera trap videos collected over approximately 10 years (2014-2024) from 741 locations across 4 continents, spanning 99 species categories. Each video is exhaustively annotated culminating in ~46 hours of densely annotated footage containing 16,224 masklet identities and 942,702 individual bounding boxes, segmentation masks, and species labels. Alongside the task-specific annotations, we publish anonymized camera trap locations for each video. Finally, we present comprehensive benchmarks on SA-FARI using state-of-the-art vision-language models for detection and tracking, including SAM 3, evaluated with both species-specific and generic animal prompts. We also compare against vision-only methods developed specifically for wildlife analysis. SA-FARI is the first large-scale dataset to combine high species diversity, multi-region coverage, and high-quality spatio-temporal annotations, offering a new foundation for advancing generalizable multianimal tracking in the wild. The dataset is available at $\href{https://www.conservationxlabs.com/sa-fari}{\text{conservationxlabs.com/SA-FARI}}$.
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            <a href="https://www.alphaxiv.org/abs/2511.15618v1" target="_blank" rel="noopener noreferrer">
                FlashMesh：通过结构化推测实现更快更好的自回归网格合成
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            FlashMesh: Faster and Better Autoregressive Mesh Synthesis via Structured Speculation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tingrui Shen, Yiheng Zhang, Chen Tang, Chuan Ping, Zixing Zhao, Le Wan, Yuwang W...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机图形学中的网格合成技术，属于纯粹的3D视觉领域，与推荐系统、搜索或广告没有直接关联。论文标题中提到的自回归方法和结构化推测技术虽然有趣，但没有显示出在推荐、搜索或广告领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 17:03:49
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                <a href="https://arxiv.org/abs/2511.15618v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15618v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Autoregressive models can generate high-quality 3D meshes by sequentially producing vertices and faces, but their token-by-token decoding results in slow inference, limiting practical use in interactive and large-scale applications. We present FlashMesh, a fast and high-fidelity mesh generation framework that rethinks autoregressive decoding through a predict-correct-verify paradigm. The key insight is that mesh tokens exhibit strong structural and geometric correlations that enable confident multi-token speculation. FlashMesh leverages this by introducing a speculative decoding scheme tailored to the commonly used hourglass transformer architecture, enabling parallel prediction across face, point, and coordinate levels. Extensive experiments show that FlashMesh achieves up to a 2 x speedup over standard autoregressive models while also improving generation fidelity. Our results demonstrate that structural priors in mesh data can be systematically harnessed to accelerate and enhance autoregressive generation.
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            <a href="https://www.alphaxiv.org/abs/2511.15603v1" target="_blank" rel="noopener noreferrer">
                MaskMed：用于医学图像分割的解耦掩码与类别预测方法
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            MaskMed: Decoupled Mask and Class Prediction for Medical Image Segmentation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bin Xie, Gady Agam
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于医学图像分割领域，属于明确的医学应用范畴，与推荐系统、搜索或广告完全无关。论文标题中提到的掩码预测和类别预测技术虽然可能涉及计算机视觉，但其特定应用于医学领域，没有任何与推荐、搜索或广告相关的潜在应用场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 16:49:02
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                <a href="https://arxiv.org/abs/2511.15603v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15603v1
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                    Medical image segmentation typically adopts a point-wise convolutional segmentation head to predict dense labels, where each output channel is heuristically tied to a specific class. This rigid design limits both feature sharing and semantic generalization. In this work, we propose a unified decoupled segmentation head that separates multi-class prediction into class-agnostic mask prediction and class label prediction using shared object queries. Furthermore, we introduce a Full-Scale Aware Deformable Transformer module that enables low-resolution encoder features to attend across full-resolution encoder features via deformable attention, achieving memory-efficient and spatially aligned full-scale fusion. Our proposed method, named MaskMed, achieves state-of-the-art performance, surpassing nnUNet by +2.0% Dice on AMOS 2022 and +6.9% Dice on BTCV.
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                US-X Complete：一种用于解剖结构三维形状恢复的多模态方法
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            US-X Complete: A Multi-Modal Approach to Anatomical 3D Shape Recovery
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Miruna-Alexandra Gafencu, Yordanka Velikova, Nassir Navab, Mohammad Farid Azampo...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向医学成像领域（解剖结构三维形状恢复），这属于明确的无关主题范畴。虽然提到了多模态方法，但应用场景是医学解剖而非推荐系统、搜索或广告领域，与所有当前关注点均无关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 16:45:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15600v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15600v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    Ultrasound offers a radiation-free, cost-effective solution for real-time visualization of spinal landmarks, paraspinal soft tissues and neurovascular structures, making it valuable for intraoperative guidance during spinal procedures. However, ultrasound suffers from inherent limitations in visualizing complete vertebral anatomy, in particular vertebral bodies, due to acoustic shadowing effects caused by bone. In this work, we present a novel multi-modal deep learning method for completing occluded anatomical structures in 3D ultrasound by leveraging complementary information from a single X-ray image. To enable training, we generate paired training data consisting of: (1) 2D lateral vertebral views that simulate X-ray scans, and (2) 3D partial vertebrae representations that mimic the limited visibility and occlusions encountered during ultrasound spine imaging. Our method integrates morphological information from both imaging modalities and demonstrates significant improvements in vertebral reconstruction (p < 0.001) compared to state of art in 3D ultrasound vertebral completion. We perform phantom studies as an initial step to future clinical translation, and achieve a more accurate, complete volumetric lumbar spine visualization overlayed on the ultrasound scan without the need for registration with preoperative modalities such as computed tomography. This demonstrates that integrating a single X-ray projection mitigates ultrasound's key limitation while preserving its strengths as the primary imaging modality. Code and data can be found at https://github.com/miruna20/US-X-Complete
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                从错误中学习：用于激光雷达地点识别的损失感知记忆增强持续学习
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            Learning from Mistakes: Loss-Aware Memory Enhanced Continual Learning for LiDAR Place Recognition
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xufei Wang, Junqiao Zhao, Siyue Tao, Qiwen Gu, Wonbong Kim, Tiantian Feng
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于激光雷达地点识别，这是计算机视觉和机器人领域的特定应用。虽然提到了持续学习技术，但其核心应用领域（激光雷达、地点识别）与搜索、推荐或广告系统没有直接关联。该技术没有明显的潜力应用于推荐系统、搜索或广告领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 16:41:30
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15597v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15597v1
                </a>
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    LiDAR place recognition plays a crucial role in SLAM, robot navigation, and autonomous driving. However, existing LiDAR place recognition methods often struggle to adapt to new environments without forgetting previously learned knowledge, a challenge widely known as catastrophic forgetting. To address this issue, we propose KDF+, a novel continual learning framework for LiDAR place recognition that extends the KDF paradigm with a loss-aware sampling strategy and a rehearsal enhancement mechanism. The proposed sampling strategy estimates the learning difficulty of each sample via its loss value and selects samples for replay according to their estimated difficulty. Harder samples, which tend to encode more discriminative information, are sampled with higher probability while maintaining distributional coverage across the dataset. In addition, the rehearsal enhancement mechanism encourages memory samples to be further refined during new-task training by slightly reducing their loss relative to previous tasks, thereby reinforcing long-term knowledge retention. Extensive experiments across multiple benchmarks demonstrate that KDF+ consistently outperforms existing continual learning methods and can be seamlessly integrated into state-of-the-art continual learning for LiDAR place recognition frameworks to yield significant and stable performance gains. The code will be available at https://github.com/repo/KDF-plus.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.15586v1" target="_blank" rel="noopener noreferrer">
                MHR：动量人体骨架
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            MHR: Momentum Human Rig
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Aaron Ferguson, Ahmed A. A. Osman, Berta Bescos, Carsten Stoll, Chris Twigg, Chr...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及计算机视觉中的人体骨架/姿态估计技术，属于纯粹的视觉领域研究。没有证据表明该技术与推荐系统、搜索或广告有任何关联，也不涉及LLM、Transformer架构或异构数据处理。动量方法在此上下文中可能指的是优化技术，但整体主题完全超出关注范围。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 16:18:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15586v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15586v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.GR</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We present MHR, a parametric human body model that combines the decoupled skeleton/shape paradigm of ATLAS with a flexible, modern rig and pose corrective system inspired by the Momentum library. Our model enables expressive, anatomically plausible human animation, supporting non-linear pose correctives, and is designed for robust integration in AR/VR and graphics pipelines.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.15580v1" target="_blank" rel="noopener noreferrer">
                CompTrack：基于信息瓶颈引导的低秩动态令牌压缩的点云跟踪方法
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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            CompTrack: Information Bottleneck-Guided Low-Rank Dynamic Token Compression for Point Cloud Tracking
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sifan Zhou, Yichao Cao, Jiahao Nie, Yuqian Fu, Ziyu Zhao, Xiaobo Lu, Shuo Wang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于点云跟踪，属于计算机视觉和3D视觉领域，与推荐系统、搜索或广告没有直接关联。虽然提到了令牌压缩技术，但这是针对点云数据的特定应用，没有显示出在RecSys/Search/Ads领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 16:12:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15580v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15580v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    3D single object tracking (SOT) in LiDAR point clouds is a critical task in computer vision and autonomous driving. Despite great success having been achieved, the inherent sparsity of point clouds introduces a dual-redundancy challenge that limits existing trackers: (1) vast spatial redundancy from background noise impairs accuracy, and (2) informational redundancy within the foreground hinders efficiency. To tackle these issues, we propose CompTrack, a novel end-to-end framework that systematically eliminates both forms of redundancy in point clouds. First, CompTrack incorporates a Spatial Foreground Predictor (SFP) module to filter out irrelevant background noise based on information entropy, addressing spatial redundancy. Subsequently, its core is an Information Bottleneck-guided Dynamic Token Compression (IB-DTC) module that eliminates the informational redundancy within the foreground. Theoretically grounded in low-rank approximation, this module leverages an online SVD analysis to adaptively compress the redundant foreground into a compact and highly informative set of proxy tokens. Extensive experiments on KITTI, nuScenes and Waymo datasets demonstrate that CompTrack achieves top-performing tracking performance with superior efficiency, running at a real-time 90 FPS on a single RTX 3090 GPU.
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            <a href="https://www.alphaxiv.org/abs/2511.15578v1" target="_blank" rel="noopener noreferrer">
                AVATAAR：通过时序自适应对齐与推理的智能体视频问答
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            AVATAAR: Agentic Video Answering via Temporal Adaptive Alignment and Reasoning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Urjitkumar Patel, Fang-Chun Yeh, Chinmay Gondhalekar
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视频问答任务，属于纯粹的视觉-语言多模态领域，与推荐系统、搜索或广告的核心技术没有直接关联。虽然提到了智能体（Agentic）和推理（Reasoning）概念，但论文的核心是视频时序理解，在推荐/搜索/广告领域没有明显的应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 16:09:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15578v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15578v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    With the increasing prevalence of video content, effectively understanding and answering questions about long form videos has become essential for numerous applications. Although large vision language models (LVLMs) have enhanced performance, they often face challenges with nuanced queries that demand both a comprehensive understanding and detailed analysis. To overcome these obstacles, we introduce AVATAAR, a modular and interpretable framework that combines global and local video context, along with a Pre Retrieval Thinking Agent and a Rethink Module. AVATAAR creates a persistent global summary and establishes a feedback loop between the Rethink Module and the Pre Retrieval Thinking Agent, allowing the system to refine its retrieval strategies based on partial answers and replicate human-like iterative reasoning. On the CinePile benchmark, AVATAAR demonstrates significant improvements over a baseline, achieving relative gains of +5.6% in temporal reasoning, +5% in technical queries, +8% in theme-based questions, and +8.2% in narrative comprehension. Our experiments confirm that each module contributes positively to the overall performance, with the feedback loop being crucial for adaptability. These findings highlight AVATAAR's effectiveness in enhancing video understanding capabilities. Ultimately, AVATAAR presents a scalable solution for long-form Video Question Answering (QA), merging accuracy, interpretability, and extensibility.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.15571v1" target="_blank" rel="noopener noreferrer">
                针对AI生成图像检测器的可迁移双域特征重要性攻击
            </a>
        </h3>
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            Transferable Dual-Domain Feature Importance Attack against AI-Generated Image Detector
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Weiheng Zhu, Gang Cao, Jing Liu, Lifang Yu, Shaowei Weng
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注AI生成图像检测器的对抗攻击，属于AIGC和安全性领域，与我的核心关注点（推荐系统、搜索、广告中的核心进展、LLM技术应用及Transformer架构改进）完全无关。论文内容涉及图像检测和攻击方法，没有显示出在推荐、搜索或广告系统中的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 16:03:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15571v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15571v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CR</span></div>
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                    Recent AI-generated image (AIGI) detectors achieve impressive accuracy under clean condition. In view of antiforensics, it is significant to develop advanced adversarial attacks for evaluating the security of such detectors, which remains unexplored sufficiently. This letter proposes a Dual-domain Feature Importance Attack (DuFIA) scheme to invalidate AIGI detectors to some extent. Forensically important features are captured by the spatially interpolated gradient and frequency-aware perturbation. The adversarial transferability is enhanced by jointly modeling spatial and frequency-domain feature importances, which are fused to guide the optimization-based adversarial example generation. Extensive experiments across various AIGI detectors verify the cross-model transferability, transparency and robustness of DuFIA.
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            <a href="https://www.alphaxiv.org/abs/2511.15565v1" target="_blank" rel="noopener noreferrer">
                Scriboora：重新思考人体姿态预测
            </a>
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            Scriboora: Rethinking Human Pose Forecasting
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Daniel Bermuth, Alexander Poeppel, Wolfgang Reif
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于人体姿态预测这一计算机视觉任务，与推荐系统、搜索或广告的核心领域进展、LLM技术或Transformer架构改进均无直接关联。人体姿态预测主要应用于运动分析、人机交互等视觉领域，无法为RecSys/Search/Ads提供明确的技术支持或潜在应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 15:58:33
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15565v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15565v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Human pose forecasting predicts future poses based on past observations, and has many significant applications in areas such as action recognition, autonomous driving or human-robot interaction. This paper evaluates a wide range of pose forecasting algorithms in the task of absolute pose forecasting, revealing many reproducibility issues, and provides a unified training and evaluation pipeline. After drawing a high-level analogy to the task of speech understanding, it is shown that recent speech models can be efficiently adapted to the task of pose forecasting, and improve current state-of-the-art performance. At last the robustness of the models is evaluated, using noisy joint coordinates obtained from a pose estimator model, to reflect a realistic type of noise, which is more close to real-world applications. For this a new dataset variation is introduced, and it is shown that estimated poses result in a substantial performance degradation, and how much of it can be recovered again by unsupervised finetuning.
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            <a href="https://www.alphaxiv.org/abs/2511.15535v1" target="_blank" rel="noopener noreferrer">
                基于GAN增强的混合CNN-ViT-GNN框架在精准农业中的智能杂草检测
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            A Hybrid CNN-ViT-GNN Framework with GAN-Based Augmentation for Intelligent Weed Detection in Precision Agriculture
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Pandiyaraju V, Abishek Karthik, Sreya Mynampati, Poovarasan L, D. Saraswathi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于精准农业中的杂草检测应用，属于农业领域的计算机视觉任务。虽然涉及CNN、ViT和GNN等架构，但应用场景与推荐系统、搜索或广告完全无关。论文的技术方法也没有显示出在RecSys/Search/Ads领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 15:32:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15535v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15535v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    The task of weed detection is an essential element of precision agriculture since accurate species identification allows a farmer to selectively apply herbicides and fits into sustainable agriculture crop management. This paper proposes a hybrid deep learning framework recipe for weed detection that utilizes Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Graph Neural Networks (GNNs) to build robustness to multiple field conditions. A Generative Adversarial Network (GAN)-based augmentation method was imposed to balance class distributions and better generalize the model. Further, a self-supervised contrastive pre-training method helps to learn more features from limited annotated data. Experimental results yield superior results with 99.33% accuracy, precision, recall, and F1-score on multi-benchmark datasets. The proposed model architecture enables local, global, and relational feature representations and offers high interpretability and adaptability. Practically, the framework allows real-time, efficient deployment to edge devices for automated weed detecting, reducing over-reliance on herbicides and providing scalable, sustainable precision-farming options.
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            <a href="https://www.alphaxiv.org/abs/2511.15496v1" target="_blank" rel="noopener noreferrer">
                跨多个强度等级评估低光图像增强
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            Evaluating Low-Light Image Enhancement Across Multiple Intensity Levels
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Maria Pilligua, David Serrano-Lozano, Pai Peng, Ramon Baldrich, Michael S. Brown...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的低光图像增强技术评估，属于纯粹的视觉处理任务。虽然视觉技术在推荐系统中可能用于图像内容理解，但论文标题明确表明其核心是图像增强的质量评估，没有涉及推荐、搜索或广告系统的任何技术要素，也没有展示与LLM、Transformer架构或异构数据建模的关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 14:52:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15496v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15496v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Imaging in low-light environments is challenging due to reduced scene radiance, which leads to elevated sensor noise and reduced color saturation. Most learning-based low-light enhancement methods rely on paired training data captured under a single low-light condition and a well-lit reference. The lack of radiance diversity limits our understanding of how enhancement techniques perform across varying illumination intensities. We introduce the Multi-Illumination Low-Light (MILL) dataset, containing images captured at diverse light intensities under controlled conditions with fixed camera settings and precise illuminance measurements. MILL enables comprehensive evaluation of enhancement algorithms across variable lighting conditions. We benchmark several state-of-the-art methods and reveal significant performance variations across intensity levels. Leveraging the unique multi-illumination structure of our dataset, we propose improvements that enhance robustness across diverse illumination scenarios. Our modifications achieve up to 10 dB PSNR improvement for DSLR and 2 dB for the smartphone on Full HD images.
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            <a href="https://www.alphaxiv.org/abs/2511.15485v1" target="_blank" rel="noopener noreferrer">
                一种基于谱特征的新型CustNetGC增强模型用于帕金森病预测
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            A Novel CustNetGC Boosted Model with Spectral Features for Parkinson's Disease Prediction
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Abishek Karthik, Pandiyaraju V, Dominic Savio M, Rohit Swaminathan S
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于帕金森病的医学预测应用，这属于明确的无关主题（医学/生物学领域）。论文标题中没有任何与推荐系统、搜索、广告或相关使能技术相关的内容，完全超出了当前关注范围。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 14:41:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15485v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15485v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.SD</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Parkinson's disease is a neurodegenerative disorder that can be very tricky to diagnose and treat. Such early symptoms can include tremors, wheezy breathing, and changes in voice quality as critical indicators of neural damage. Notably, there has been growing interest in utilizing changes in vocal attributes as markers for the detection of PD early on. Based on this understanding, the present paper was designed to focus on the acoustic feature analysis based on voice recordings of patients diagnosed with PD and healthy controls (HC). In this paper, we introduce a novel classification and visualization model known as CustNetGC, combining a Convolutional Neural Network (CNN) with Custom Network Grad-CAM and CatBoost to enhance the efficiency of PD diagnosis. We use a publicly available dataset from Figshare, including voice recordings of 81 participants: 40 patients with PD and 41 healthy controls. From these recordings, we extracted the key spectral features: L-mHP and Spectral Slopes. The L-mHP feature combines three spectrogram representations: Log-Mel spectrogram, harmonic spectrogram, and percussive spectrogram, which are derived using Harmonic-Percussive Source Separation (HPSS). Grad-CAM was used to highlight the important regions in the data, thus making the PD predictions interpretable and effective. Our proposed CustNetGC model achieved an accuracy of 99.06% and precision of 95.83%, with the area under the ROC curve (AUC) recorded at 0.90 for the PD class and 0.89 for the HC class. Additionally, the combination of CatBoost, a gradient boosting algorithm, enhanced the robustness and the prediction performance by properly classifying PD and non-PD samples. Therefore, the results provide the potential improvement in the CustNetGC system in enhancing diagnostic accuracy and the interpretability of the Parkinson's Disease prediction model.
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            <a href="https://www.alphaxiv.org/abs/2511.15481v1" target="_blank" rel="noopener noreferrer">
                FunnyNodules：一个用于评估可解释人工智能的定制化医学数据集
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            FunnyNodules: A Customizable Medical Dataset Tailored for Evaluating Explainable AI
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Luisa Gallée, Yiheng Xiong, Meinrad Beer, Michael Götz
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的定制数据集创建，用于评估可解释AI，这属于明确的无关主题（医学领域应用）。论文内容与推荐系统、搜索、广告或相关使能技术没有任何关联，也没有展示在相关领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 14:37:49
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15481v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15481v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Densely annotated medical image datasets that capture not only diagnostic labels but also the underlying reasoning behind these diagnoses are scarce. Such reasoning-related annotations are essential for developing and evaluating explainable AI (xAI) models that reason similarly to radiologists: making correct predictions for the right reasons. To address this gap, we introduce FunnyNodules, a fully parameterized synthetic dataset designed for systematic analysis of attribute-based reasoning in medical AI models. The dataset generates abstract, lung nodule-like shapes with controllable visual attributes such as roundness, margin sharpness, and spiculation. Target class is derived from a predefined attribute combination, allowing full control over the decision rule that links attributes to the diagnostic class. We demonstrate how FunnyNodules can be used in model-agnostic evaluations to assess whether models learn correct attribute-target relations, to interpret over- or underperformance in attribute prediction, and to analyze attention alignment with attribute-specific regions of interest. The framework is fully customizable, supporting variations in dataset complexity, target definitions, class balance, and beyond. With complete ground truth information, FunnyNodules provides a versatile foundation for developing, benchmarking, and conducting in-depth analyses of explainable AI methods in medical image analysis.
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            <a href="https://www.alphaxiv.org/abs/2511.15476v1" target="_blank" rel="noopener noreferrer">
                RS-CA-HSICT：一种用于猴痘检测的残差与空间通道增强CNN Transformer框架
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            RS-CA-HSICT: A Residual and Spatial Channel Augmented CNN Transformer Framework for Monkeypox Detection
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Rashid Iqbal, Saddam Hussain Khan
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学图像中的猴痘检测，属于明确的医学领域应用。虽然使用了Transformer架构，但其应用场景（医学诊断）和具体任务（疾病检测）完全超出了当前关注的推荐系统、搜索或广告领域，没有任何潜在的应用关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 14:32:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15476v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15476v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                    This work proposes a hybrid deep learning approach, namely Residual and Spatial Learning based Channel Augmented Integrated CNN-Transformer architecture, that leverages the strengths of CNN and Transformer towards enhanced MPox detection. The proposed RS-CA-HSICT framework is composed of an HSICT block, a residual CNN module, a spatial CNN block, and a CA, which enhances the diverse feature space, detailed lesion information, and long-range dependencies. The new HSICT module first integrates an abstract representation of the stem CNN and customized ICT blocks for efficient multihead attention and structured CNN layers with homogeneous (H) and structural (S) operations. The customized ICT blocks learn global contextual interactions and local texture extraction. Additionally, H and S layers learn spatial homogeneity and fine structural details by reducing noise and modeling complex morphological variations. Moreover, inverse residual learning enhances vanishing gradient, and stage-wise resolution reduction ensures scale invariance. Furthermore, the RS-CA-HSICT framework augments the learned HSICT channels with the TL-driven Residual and Spatial CNN maps for enhanced multiscale feature space capturing global and localized structural cues, subtle texture, and contrast variations. These channels, preceding augmentation, are refined through the Channel-Fusion-and-Attention block, which preserves discriminative channels while suppressing redundant ones, thereby enabling efficient computation. Finally, the spatial attention mechanism refines pixel selection to detect subtle patterns and intra-class contrast variations in Mpox. Experimental results on both the Kaggle benchmark and a diverse MPox dataset reported classification accuracy as high as 98.30% and an F1-score of 98.13%, which outperforms the existing CNNs and ViTs.
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            <a href="https://www.alphaxiv.org/abs/2511.15468v1" target="_blank" rel="noopener noreferrer">
                基于深度学习的金枪鱼围网捕捞热带水域渔获物组成精确视觉识别
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            Deep Learning for Accurate Vision-based Catch Composition in Tropical Tuna Purse Seiners
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xabier Lekunberri, Ahmad Kamal, Izaro Goienetxea, Jon Ruiz, Iñaki Quincoces, Jai...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉在渔业领域的特定应用，与推荐系统、搜索或广告领域没有任何关联。论文内容涉及海洋生物学和渔业管理，属于明确的领域特定应用，完全超出了当前关注的技术范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 14:26:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15468v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15468v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Purse seiners play a crucial role in tuna fishing, as approximately 69% of the world's tropical tuna is caught using this gear. All tuna Regional Fisheries Management Organizations have established minimum standards to use electronic monitoring (EM) in fisheries in addition to traditional observers. The EM systems produce a massive amount of video data that human analysts must process. Integrating artificial intelligence (AI) into their workflow can decrease that workload and improve the accuracy of the reports. However, species identification still poses significant challenges for AI, as achieving balanced performance across all species requires appropriate training data. Here, we quantify the difficulty experts face to distinguish bigeye tuna (BET, Thunnus Obesus) from yellowfin tuna (YFT, Thunnus Albacares) using images captured by EM systems. We found inter-expert agreements of 42.9% $\pm$ 35.6% for BET and 57.1% $\pm$ 35.6% for YFT. We then present a multi-stage pipeline to estimate the species composition of the catches using a reliable ground-truth dataset based on identifications made by observers on board. Three segmentation approaches are compared: Mask R-CNN, a combination of DINOv2 with SAM2, and a integration of YOLOv9 with SAM2. We found that the latest performs the best, with a validation mean average precision of 0.66 $\pm$ 0.03 and a recall of 0.88 $\pm$ 0.03. Segmented individuals are tracked using ByteTrack. For classification, we evaluate a standard multiclass classification model and a hierarchical approach, finding a superior generalization by the hierarchical. All our models were cross-validated during training and tested on fishing operations with fully known catch composition. Combining YOLOv9-SAM2 with the hierarchical classification produced the best estimations, with 84.8% of the individuals being segmented and classified with a mean average error of 4.5%.
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            <a href="https://www.alphaxiv.org/abs/2511.15464v1" target="_blank" rel="noopener noreferrer">
                SIGMMA：基于层次化图结构的多尺度多模态组织病理学图像与空间转录组对比对齐方法
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            SIGMMA: Hierarchical Graph-Based Multi-Scale Multi-modal Contrastive Alignment of Histopathology Image and Spatial Transcriptome
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dabin Jeong, Amirhossein Vahidi, Ciro Ramírez-Suástegui, Marie Moullet, Kevin Ly...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于组织病理学图像和空间转录组的医学领域多模态对齐，属于生物医学应用范畴。虽然涉及多模态对比学习技术，但其应用场景明确限定在医疗领域，与推荐系统、搜索或广告的技术栈和应用需求完全无关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 14:22:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15464v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15464v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent advances in computational pathology have leveraged vision-language models to learn joint representations of Hematoxylin and Eosin (HE) images with spatial transcriptomic (ST) profiles. However, existing approaches typically align HE tiles with their corresponding ST profiles at a single scale, overlooking fine-grained cellular structures and their spatial organization. To address this, we propose Sigmma, a multi-modal contrastive alignment framework for learning hierarchical representations of HE images and spatial transcriptome profiles across multiple scales. Sigmma introduces multi-scale contrastive alignment, ensuring that representations learned at different scales remain coherent across modalities. Furthermore, by representing cell interactions as a graph and integrating inter- and intra-subgraph relationships, our approach effectively captures cell-cell interactions, ranging from fine to coarse, within the tissue microenvironment. We demonstrate that Sigmm learns representations that better capture cross-modal correspondences, leading to an improvement of avg. 9.78\% in the gene-expression prediction task and avg. 26.93\% in the cross-modal retrieval task across datasets. We further show that it learns meaningful multi-tissue organization in downstream analyses.
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            <a href="https://www.alphaxiv.org/abs/2511.15459v1" target="_blank" rel="noopener noreferrer">
                脉冲驱动：一种面向脉冲相机的熵引导目标检测器
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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            Driving in Spikes: An Entropy-Guided Object Detector for Spike Cameras
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ziyan Liu, Qi Su, Lulu Tang, Zhaofei Yu, Tiejun Huang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于脉冲相机的目标检测技术，属于纯粹的计算机视觉领域，与推荐系统、搜索或广告的核心技术栈没有直接关联。脉冲相机是高度专业化的视觉传感器，其应用场景和技术路径与文本/序列建模为主的RecSys/Search/Ads领域相距甚远，无法识别出任何潜在的应用连接点。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 14:16:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15459v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15459v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Object detection in autonomous driving suffers from motion blur and saturation under fast motion and extreme lighting. Spike cameras, offer microsecond latency and ultra high dynamic range for object detection by using per pixel asynchronous integrate and fire. However, their sparse, discrete output cannot be processed by standard image-based detectors, posing a critical challenge for end to end spike stream detection. We propose EASD, an end to end spike camera detector with a dual branch design: a Temporal Based Texture plus Feature Fusion branch for global cross slice semantics, and an Entropy Selective Attention branch for object centric details. To close the data gap, we introduce DSEC Spike, the first driving oriented simulated spike detection benchmark.
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            <a href="https://www.alphaxiv.org/abs/2511.15440v1" target="_blank" rel="noopener noreferrer">
                再制造中基于深度学习的视觉质量检测数据集与基线方法
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        <div class="mb-2 text-base text-gray-700">
            A Dataset and Baseline for Deep Learning-Based Visual Quality Inspection in Remanufacturing
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Johannes C. Bauer, Paul Geng, Stephan Trattnig, Petr Dokládal, Rüdiger Daub
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于工业制造领域的视觉质量检测应用，属于纯粹的计算机视觉任务。虽然涉及深度学习技术，但其应用场景（再制造质量检测）与推荐系统、搜索或广告领域没有任何关联，也不涉及LLM技术或Transformer架构的进展。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 13:56:33
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15440v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15440v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Remanufacturing describes a process where worn products are restored to like-new condition and it offers vast ecological and economic potentials. A key step is the quality inspection of disassembled components, which is mostly done manually due to the high variety of parts and defect patterns. Deep neural networks show great potential to automate such visual inspection tasks but struggle to generalize to new product variants, components, or defect patterns. To tackle this challenge, we propose a novel image dataset depicting typical gearbox components in good and defective condition from two automotive transmissions. Depending on the train-test split of the data, different distribution shifts are generated to benchmark the generalization ability of a classification model. We evaluate different models using the dataset and propose a contrastive regularization loss to enhance model robustness. The results obtained demonstrate the ability of the loss to improve generalisation to unseen types of components.
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            <a href="https://www.alphaxiv.org/abs/2511.15429v1" target="_blank" rel="noopener noreferrer">
                WarNav：用于战争场景中可导航区域分割的自动驾驶基准
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            WarNav: An Autonomous Driving Benchmark for Segmentation of Navigable Zones in War Scenes
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Marc-Emmanuel Coupvent des Graviers, Hejer Ammar, Christophe Guettier, Yann Dumo...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于战争场景下的自动驾驶和分割任务，属于纯粹的计算机视觉领域，与推荐系统、搜索或广告没有任何关联。论文内容涉及特定领域的视觉基准测试，没有展示出在推荐、搜索或广告系统中的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 13:32:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15429v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15429v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We introduce WarNav, a novel real-world dataset constructed from images of the open-source DATTALION repository, specifically tailored to enable the development and benchmarking of semantic segmentation models for autonomous ground vehicle navigation in unstructured, conflict-affected environments. This dataset addresses a critical gap between conventional urban driving resources and the unique operational scenarios encountered by unmanned systems in hazardous and damaged war-zones. We detail the methodological challenges encountered, ranging from data heterogeneity to ethical considerations, providing guidance for future efforts that target extreme operational contexts. To establish performance references, we report baseline results on WarNav using several state-of-the-art semantic segmentation models trained on structured urban scenes. We further analyse the impact of training data environments and propose a first step towards effective navigability in challenging environments with the constraint of having no annotation of the targeted images. Our goal is to foster impactful research that enhances the robustness and safety of autonomous vehicles in high-risk scenarios while being frugal in annotated data.
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            <a href="https://www.alphaxiv.org/abs/2511.15407v1" target="_blank" rel="noopener noreferrer">
                IPR-1：交互式物理推理器
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        <div class="mb-2 text-base text-gray-700">
            IPR-1: Interactive Physical Reasoner
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mingyu Zhang, Lifeng Zhuo, Tianxi Tan, Guocan Xie, Xian Nie, Yan Li, Renjie Zhao...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明研究重点是物理推理和交互系统，这与推荐系统、搜索或广告的核心领域没有直接关联。物理推理属于机器人学或物理模拟领域，无法应用于推荐、搜索或广告中的任何技术方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 13:04:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15407v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15407v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                    Humans learn by observing, interacting with environments, and internalizing physics and causality. Here, we aim to ask whether an agent can similarly acquire human-like reasoning from interaction and keep improving with more experience. We study this in a Game-to-Unseen (G2U) setting, curating 1,000+ heterogeneous games with diverse physical and causal mechanisms, and evaluate at three human-like levels: Survival, Curiosity, Utility, from primitive intuition to goal-driven reasoning. Our analysis reveals complementary failures: VLM/VLA agents reason but lack look-ahead in interactive settings, while world models imagine but imitate visual patterns rather than analyze physics and causality. We therefore propose IPR (Interactive Physical Reasoner), using world-model rollouts to score and reinforce a VLM's policy, and introduce PhysCode, a physics-centric action code aligning semantic intent with dynamics to provide a shared action space for prediction and reasoning. Pretrained on 1,000+ games, our IPR performs robustly on three levels, matches GPT-5 overall, and surpasses it on Curiosity. We find that performance improves with more training games and interaction steps, and that the model also zero-shot transfers to unseen games. These results support physics-centric interaction as a path to steadily improving physical reasoning.
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            <a href="https://www.alphaxiv.org/abs/2511.15343v1" target="_blank" rel="noopener noreferrer">
                用于3类开放集航空目标检测的快速事后置信度融合方法
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            Fast Post-Hoc Confidence Fusion for 3-Class Open-Set Aerial Object Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Spyridon Loukovitis, Vasileios Karampinis, Athanasios Voulodimos
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D航空目标检测和开放集识别问题，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术没有直接关联。论文标题中提到的置信度融合方法虽然涉及不确定性建模，但其应用场景（航空目标检测）和问题设定（3类开放集）过于特定，无法迁移到RecSys/Search/Ads领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 11:03:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15343v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15343v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span><span class="category-tag">cs.RO</span></div>
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                    Developing reliable UAV navigation systems requires robust air-to-air object detectors capable of distinguishing between objects seen during training and previously unseen objects. While many methods address closed-set detection and achieve high-confidence recognition of in-domain (ID) targets, they generally do not tackle open-set detection, which requires simultaneous handling of both ID and out-of-distribution (OOD) objects. Existing open-set approaches typically rely on a single uncertainty score with thresholding, limiting flexibility and often conflating OOD objects with background clutter. In contrast, we propose a lightweight, model-agnostic post-processing framework that explicitly separates background from unknown objects while preserving the base detector's performance. Our approach extends open-set detection beyond binary ID/OOD classification to real-time three-way classification among ID targets, OOD objects, and background. To this end, we employ a fusion scheme that aggregates multiple confidence estimates and per-detection features using a compact multilayer perceptron (MLP). Incorporating different logit variants into the MLP consistently enhances performance across both binary and three-class classification without compromising throughput. Extensive ablation and comparative experiments confirm that our method surpasses threshold-based baselines in two-class classification by an average of 2.7% AUROC, while retaining or improving open-set mAP. Furthermore, our study uniquely enables robust three-class classification, a critical capability for safe UAV navigation, where OOD objects must be actively avoided and background regions safely ignored. Comparative analysis highlights that our method surpasses competitive techniques in AUROC across datasets, while improving closed-set mAP by up to 9 points, an 18% relative gain.
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                用于指纹伪造检测的自适应阈值模式
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            Adaptive thresholding pattern for fingerprint forgery detection
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zahra Farzadpour, Masoumeh Azghani
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确涉及指纹检测技术，这属于生物识别安全领域，与我的关注点完全无关。指纹、联邦学习、安全和隐私等主题已被明确列为不相关内容，因此该论文在推荐系统、搜索或广告方面没有任何潜在应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 10:39:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15322v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15322v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Fingerprint liveness detection systems have been affected by spoofing, which is a severe threat for fingerprint-based biometric systems. Therefore, it is crucial to develop some techniques to distinguish the fake fingerprints from the real ones. The software based techniques can detect the fingerprint forgery automatically. Also, the scheme shall be resistant against various distortions such as noise contamination, pixel missing and block missing, so that the forgers cannot deceive the detector by adding some distortions to the faked fingerprint. In this paper, we propose a fingerprint forgery detection algorithm based on a suggested adaptive thresholding pattern. The anisotropic diffusion of the input image is passed through three levels of the wavelet transform. The coefficients of different layers are adaptively thresholded and concatenated to produce the feature vector which is classified using the SVM classifier. Another contribution of the paper is to investigate the effect of various distortions such as pixel missing, block missing, and noise contamination. Our suggested approach includes a novel method that exhibits improved resistance against a range of distortions caused by environmental phenomena or manipulations by malicious users. In quantitative comparisons, our proposed method outperforms its counterparts by approximately 8% and 5% in accuracy for missing pixel scenarios of 90% and block missing scenarios of size 70x70 , respectively. This highlights the novelty approach in addressing such challenges.
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            <a href="https://www.alphaxiv.org/abs/2511.15316v1" target="_blank" rel="noopener noreferrer">
                你的特征揭示了什么：面向拆分深度神经网络的数据高效黑盒特征反演攻击
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            <i class="fa fa-star mr-1"></i>1/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            What Your Features Reveal: Data-Efficient Black-Box Feature Inversion Attack for Split DNNs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhihan Ren, Lijun He, Jiaxi Liang, Xinzhu Fu, Haixia Bi, Fan Li
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于隐私和安全攻击方法（特征反演攻击），这明确属于被排除的隐私和安全相关主题。虽然涉及深度神经网络和特征分析，但其核心关注点是安全漏洞而非推荐系统、搜索或广告的技术进步。论文内容与当前关注的领域进展、LLM技术或Transformer架构改进没有任何关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 10:30:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15316v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15316v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Split DNNs enable edge devices by offloading intensive computation to a cloud server, but this paradigm exposes privacy vulnerabilities, as the intermediate features can be exploited to reconstruct the private inputs via Feature Inversion Attack (FIA). Existing FIA methods often produce limited reconstruction quality, making it difficult to assess the true extent of privacy leakage. To reveal the privacy risk of the leaked features, we introduce FIA-Flow, a black-box FIA framework that achieves high-fidelity image reconstruction from intermediate features. To exploit the semantic information within intermediate features, we design a Latent Feature Space Alignment Module (LFSAM) to bridge the semantic gap between the intermediate feature space and the latent space. Furthermore, to rectify distributional mismatch, we develop Deterministic Inversion Flow Matching (DIFM), which projects off-manifold features onto the target manifold with one-step inference. This decoupled design simplifies learning and enables effective training with few image-feature pairs. To quantify privacy leakage from a human perspective, we also propose two metrics based on a large vision-language model. Experiments show that FIA-Flow achieves more faithful and semantically aligned feature inversion across various models (AlexNet, ResNet, Swin Transformer, DINO, and YOLO11) and layers, revealing a more severe privacy threat in Split DNNs than previously recognized.
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            <a href="https://www.alphaxiv.org/abs/2511.15299v1" target="_blank" rel="noopener noreferrer">
                驯服用于X射线违禁物品检测的生成式合成数据
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Taming Generative Synthetic Data for X-ray Prohibited Item Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jialong Sun, Hongguang Zhu, Weizhe Liu, Yunda Sun, Renshuai Tao, Yunchao Wei
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于X射线图像检测和生成式合成数据，这属于计算机视觉在安全检测领域的特定应用。与我的关注点（推荐系统、搜索、广告中的LLM和Transformer技术）没有直接关联，且X射线违禁物品检测属于安全领域而非RecSys/Search/Ads范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 10:07:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15299v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15299v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Training prohibited item detection models requires a large amount of X-ray security images, but collecting and annotating these images is time-consuming and laborious. To address data insufficiency, X-ray security image synthesis methods composite images to scale up datasets. However, previous methods primarily follow a two-stage pipeline, where they implement labor-intensive foreground extraction in the first stage and then composite images in the second stage. Such a pipeline introduces inevitable extra labor cost and is not efficient. In this paper, we propose a one-stage X-ray security image synthesis pipeline (Xsyn) based on text-to-image generation, which incorporates two effective strategies to improve the usability of synthetic images. The Cross-Attention Refinement (CAR) strategy leverages the cross-attention map from the diffusion model to refine the bounding box annotation. The Background Occlusion Modeling (BOM) strategy explicitly models background occlusion in the latent space to enhance imaging complexity. To the best of our knowledge, compared with previous methods, Xsyn is the first to achieve high-quality X-ray security image synthesis without extra labor cost. Experiments demonstrate that our method outperforms all previous methods with 1.2% mAP improvement, and the synthetic images generated by our method are beneficial to improve prohibited item detection performance across various X-ray security datasets and detectors. Code is available at https://github.com/pILLOW-1/Xsyn/.
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            <a href="https://www.alphaxiv.org/abs/2511.15279v1" target="_blank" rel="noopener noreferrer">
                观察、缩放、理解：用于具身感知的机器人眼球
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        <div class="mb-2 text-base text-gray-700">
            Look, Zoom, Understand: The Robotic Eyeball for Embodied Perception
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiashu Yang, Yifan Han, Yucheng Xie, Ning Guo, Wenzhao Lian
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于机器人视觉和具身感知系统，属于纯粹的机器人视觉领域。虽然标题提到'理解'，但核心是机器人眼球硬件和视觉感知，与推荐系统、搜索或广告的排名和建模需求没有直接关联。该技术主要面向物理世界交互，而非数字环境中的用户行为建模或内容理解。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 09:42:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15279v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15279v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.CV</span></div>
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                    In embodied AI perception systems, visual perception should be active: the goal is not to passively process static images, but to actively acquire more informative data within pixel and spatial budget constraints. Existing vision models and fixed RGB-D camera systems fundamentally fail to reconcile wide-area coverage with fine-grained detail acquisition, severely limiting their efficacy in open-world robotic applications. To address this issue, we propose EyeVLA, a robotic eyeball for active visual perception that can take proactive actions based on instructions, enabling clear observation of fine-grained target objects and detailed information across a wide spatial extent. EyeVLA discretizes action behaviors into action tokens and integrates them with vision-language models (VLMs) that possess strong open-world understanding capabilities, enabling joint modeling of vision, language, and actions within a single autoregressive sequence. By using the 2D bounding box coordinates to guide the reasoning chain and applying reinforcement learning to refine the viewpoint selection policy, we transfer the open-world scene understanding capability of the VLM to a vision language action (VLA) policy using only minimal real-world data. Experiments show that our system efficiently performs instructed scenes in real-world environments and actively acquires more accurate visual information through instruction-driven actions of rotation and zoom, thereby achieving strong environmental perception capabilities. EyeVLA introduces a novel robotic vision system that leverages detailed and spatially rich, large-scale embodied data, and actively acquires highly informative visual observations for downstream embodied tasks.
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            <a href="https://www.alphaxiv.org/abs/2511.15271v1" target="_blank" rel="noopener noreferrer">
                用于汽车雷达目标检测的图查询网络
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        <div class="mb-2 text-base text-gray-700">
            Graph Query Networks for Object Detection with Automotive Radar
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Loveneet Saini, Hasan Tercan, Tobias Meisen
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于汽车雷达的目标检测，这属于自动驾驶和计算机视觉领域，与推荐系统、搜索或广告的核心技术没有明显关联。论文中提到的图查询网络虽然是一种神经网络架构，但其应用场景（汽车雷达）和问题领域（目标检测）完全超出了您关注的RecSys/Search/Ads范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 09:36:49
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15271v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15271v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    Object detection with 3D radar is essential for 360-degree automotive perception, but radar's long wavelengths produce sparse and irregular reflections that challenge traditional grid and sequence-based convolutional and transformer detectors. This paper introduces Graph Query Networks (GQN), an attention-based framework that models objects sensed by radar as graphs, to extract individualized relational and contextual features. GQN employs a novel concept of graph queries to dynamically attend over the bird's-eye view (BEV) space, constructing object-specific graphs processed by two novel modules: EdgeFocus for relational reasoning and DeepContext Pooling for contextual aggregation. On the NuScenes dataset, GQN improves relative mAP by up to +53%, including a +8.2% gain over the strongest prior radar method, while reducing peak graph construction overhead by 80% with moderate FLOPs cost.
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            <a href="https://www.alphaxiv.org/abs/2511.15242v1" target="_blank" rel="noopener noreferrer">
                SkinGPT-R1：仅使用适配器的双重蒸馏用于高效皮肤病学推理
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            SkinGPT-R1: Adapter-Only Dual Distillation for Efficient Dermatology Reasoning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuhao Shen, Jiahe Qian, Zhangtianyi Chen, Yuanhao He, Juexiao Zhou
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于皮肤病学领域的医学应用，这属于明确的无关主题范畴。虽然提到了适配器和蒸馏技术，但这些技术本身是通用的，但论文的特定应用领域（皮肤病学）与搜索、推荐或广告系统没有任何关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 08:55:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15242v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15242v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    We present SkinGPT-R1, a dermatology focused vision language model that makes diagnostic chain of thought reasoning explicit, step by step, and verifiable. To support skin specific reasoning, we build DermCoT, a corpus of standardized dermatologic chain of thought narratives that combines 10,000 DermEval filtered training cases with 3,000 dermatologist scored certified cases, and we define DermEval as a physician aligned six dimensional evaluator and DermBench as the corresponding benchmark for dermatologic chain of thought quality. On DermBench, across 14 general, reasoning, and medical vision language models, SkinGPT-R1 achieves an average score of 4.031 out of 5 over the six clinician defined dimensions, ranks 1st among all systems, and improves the average score over Vision-R1 by about 41%. On three dermatology classification benchmarks, SkinGPT-R1 delivers stable accuracy gains over Vision-R1 and remains competitive among strong vision language models. Ablation results further show that DermCoT based chain of thought supervision provides substantial improvements over the base model and that adding dermatology aware visual distillation yields consistent additional gains in both narrative quality and recognition.
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                基于物理原理的多模态合成图像基准评估指标
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        <div class="mb-2 text-base text-gray-700">
            Physics-Based Benchmarking Metrics for Multimodal Synthetic Images
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kishor Datta Gupta, Marufa Kamal, Md. Mahfuzur Rahman, Fahad Rahman, Mohd Ariful...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的合成图像质量评估，使用物理原理作为基准指标。虽然涉及多模态概念，但其核心是纯粹的视觉质量评估，与推荐系统、搜索或广告中的排名、用户建模或内容理解没有直接关联。论文没有展示在RecSys/Search/Ads领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 07:52:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15204v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15204v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Current state of the art measures like BLEU, CIDEr, VQA score, SigLIP-2 and CLIPScore are often unable to capture semantic or structural accuracy, especially for domain-specific or context-dependent scenarios. For this, this paper proposes a Physics-Constrained Multimodal Data Evaluation (PCMDE) metric combining large language models with reasoning, knowledge based mapping and vision-language models to overcome these limitations. The architecture is comprised of three main stages: (1) feature extraction of spatial and semantic information with multimodal features through object detection and VLMs; (2) Confidence-Weighted Component Fusion for adaptive component-level validation; and (3) physics-guided reasoning using large language models for structural and relational constraints (e.g., alignment, position, consistency) enforcement.
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            <a href="https://www.alphaxiv.org/abs/2511.15188v1" target="_blank" rel="noopener noreferrer">
                BrainRotViT：用于从3D结构磁共振成像进行可解释性脑衰老建模的Transformer-ResNet混合架构
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            BrainRotViT: Transformer-ResNet Hybrid for Explainable Modeling of Brain Aging from 3D sMRI
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wasif Jalal, Md Nafiu Rahman, M. Sohel Rahman
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的脑衰老建模，使用3D结构磁共振成像数据，这属于明确的无关主题范畴。虽然涉及Transformer架构，但其应用场景是医学影像分析，与推荐系统、搜索或广告领域没有任何关联。该研究没有展示任何在RecSys/Search/Ads领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 07:20:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15188v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15188v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    Accurate brain age estimation from structural MRI is a valuable biomarker for studying aging and neurodegeneration. Traditional regression and CNN-based methods face limitations such as manual feature engineering, limited receptive fields, and overfitting on heterogeneous data. Pure transformer models, while effective, require large datasets and high computational cost. We propose Brain ResNet over trained Vision Transformer (BrainRotViT), a hybrid architecture that combines the global context modeling of vision transformers (ViT) with the local refinement of residual CNNs. A ViT encoder is first trained on an auxiliary age and sex classification task to learn slice-level features. The frozen encoder is then applied to all sagittal slices to generate a 2D matrix of embedding vectors, which is fed into a residual CNN regressor that incorporates subject sex at the final fully-connected layer to estimate continuous brain age. Our method achieves an MAE of 3.34 years (Pearson $r=0.98$, Spearman $ρ=0.97$, $R^2=0.95$) on validation across 11 MRI datasets encompassing more than 130 acquisition sites, outperforming baseline and state-of-the-art models. It also generalizes well across 4 independent cohorts with MAEs between 3.77 and 5.04 years. Analyses on the brain age gap (the difference between the predicted age and actual age) show that aging patterns are associated with Alzheimer's disease, cognitive impairment, and autism spectrum disorder. Model attention maps highlight aging-associated regions of the brain, notably the cerebellar vermis, precentral and postcentral gyri, temporal lobes, and medial superior frontal gyrus. Our results demonstrate that this method provides an efficient, interpretable, and generalizable framework for brain-age prediction, bridging the gap between CNN- and transformer-based approaches while opening new avenues for aging and neurodegeneration research.
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            <a href="https://www.alphaxiv.org/abs/2511.15186v1" target="_blank" rel="noopener noreferrer">
                基于自动生成大规模数据集的指令引导胸部X光病灶分割
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            Instruction-Guided Lesion Segmentation for Chest X-rays with Automatically Generated Large-Scale Dataset
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Geon Choi, Hangyul Yoon, Hyunju Shin, Hyunki Park, Sang Hoon Seo, Eunho Yang, Ed...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像（胸部X光）的病灶分割任务，属于医疗领域特定应用，与推荐系统、搜索或广告无关。论文涉及指令引导分割和自动数据集生成，但这些技术应用场景局限于医疗影像分析，没有明显的RecSys/Search/Ads应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 07:17:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15186v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15186v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    The applicability of current lesion segmentation models for chest X-rays (CXRs) has been limited both by a small number of target labels and the reliance on long, detailed expert-level text inputs, creating a barrier to practical use. To address these limitations, we introduce a new paradigm: instruction-guided lesion segmentation (ILS), which is designed to segment diverse lesion types based on simple, user-friendly instructions. Under this paradigm, we construct MIMIC-ILS, the first large-scale instruction-answer dataset for CXR lesion segmentation, using our fully automated multimodal pipeline that generates annotations from chest X-ray images and their corresponding reports. MIMIC-ILS contains 1.1M instruction-answer pairs derived from 192K images and 91K unique segmentation masks, covering seven major lesion types. To empirically demonstrate its utility, we introduce ROSALIA, a vision-language model fine-tuned on MIMIC-ILS. ROSALIA can segment diverse lesions and provide textual explanations in response to user instructions. The model achieves high segmentation and textual accuracy in our newly proposed task, highlighting the effectiveness of our pipeline and the value of MIMIC-ILS as a foundational resource for pixel-level CXR lesion grounding.
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            <a href="https://www.alphaxiv.org/abs/2511.15173v1" target="_blank" rel="noopener noreferrer">
                基于叶片表型和光合性状数据驱动预测物种特异性植物对光谱转换薄膜的响应
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            Data-driven Prediction of Species-Specific Plant Responses to Spectral-Shifting Films from Leaf Phenotypic and Photosynthetic Traits
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jun Hyeun Kang, Jung Eek Son, Tae In Ahn
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于植物科学和农业技术领域，涉及植物对光谱变化的生理响应预测，与推荐系统、搜索、广告或LLM技术完全无关。论文内容属于生物科学应用范畴，不在任何相关技术焦点范围内。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 06:51:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15173v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15173v1
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                <div class="flex flex-wrap"><span class="category-tag">q-bio.QM</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span><span class="category-tag">eess.IV</span></div>
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                    The application of spectral-shifting films in greenhouses to shift green light to red light has shown variable growth responses across crop species. However, the yield enhancement of crops under altered light quality is related to the collective effects of the specific biophysical characteristics of each species. Considering only one attribute of a crop has limitations in understanding the relationship between sunlight quality adjustments and crop growth performance. Therefore, this study aims to comprehensively link multiple plant phenotypic traits and daily light integral considering the physiological responses of crops to their growth outcomes under SF using artificial intelligence. Between 2021 and 2024, various leafy, fruiting, and root crops were grown in greenhouses covered with either PEF or SF, and leaf reflectance, leaf mass per area, chlorophyll content, daily light integral, and light saturation point were measured from the plants cultivated in each condition. 210 data points were collected, but there was insufficient data to train deep learning models, so a variational autoencoder was used for data augmentation. Most crop yields showed an average increase of 22.5% under SF. These data were used to train several models, including logistic regression, decision tree, random forest, XGBoost, and feedforward neural network (FFNN), aiming to binary classify whether there was a significant effect on yield with SF application. The FFNN achieved a high classification accuracy of 91.4% on a test dataset that was not used for training. This study provide insight into the complex interactions between leaf phenotypic and photosynthetic traits, environmental conditions, and solar spectral components by improving the ability to predict solar spectral shift effects using SF.
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            <a href="https://www.alphaxiv.org/abs/2511.15167v1" target="_blank" rel="noopener noreferrer">
                从过往自身学习深度：用于鲁棒深度估计的自进化对比
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            Learning Depth from Past Selves: Self-Evolution Contrast for Robust Depth Estimation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jing Cao, Kui Jiang, Shenyi Li, Xiaocheng Feng, Yong Huang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的深度估计任务，属于纯粹的视觉感知问题，与推荐系统、搜索或广告的核心技术栈没有直接关联。论文提出的自进化对比方法虽然可能在模型鲁棒性方面有所创新，但这种视觉感知技术难以转化为推荐、搜索或广告领域的应用场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 06:42:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15167v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15167v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Self-supervised depth estimation has gained significant attention in autonomous driving and robotics. However, existing methods exhibit substantial performance degradation under adverse weather conditions such as rain and fog, where reduced visibility critically impairs depth prediction. To address this issue, we propose a novel self-evolution contrastive learning framework called SEC-Depth for self-supervised robust depth estimation tasks. Our approach leverages intermediate parameters generated during training to construct temporally evolving latency models. Using these, we design a self-evolution contrastive scheme to mitigate performance loss under challenging conditions. Concretely, we first design a dynamic update strategy of latency models for the depth estimation task to capture optimization states across training stages. To effectively leverage latency models, we introduce a self-evolution contrastive Loss (SECL) that treats outputs from historical latency models as negative samples. This mechanism adaptively adjusts learning objectives while implicitly sensing weather degradation severity, reducing the needs for manual intervention. Experiments show that our method integrates seamlessly into diverse baseline models and significantly enhances robustness in zero-shot evaluations.
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            <a href="https://www.alphaxiv.org/abs/2511.15151v1" target="_blank" rel="noopener noreferrer">
                DCL-SE：用于脑成像时空编码的动态课程学习
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            DCL-SE: Dynamic Curriculum Learning for Spatiotemporal Encoding of Brain Imaging
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Meihua Zhou, Xinyu Tong, Jiarui Zhao, Min Cheng, Li Yang, Lei Tian, Nan Wan
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于脑成像的时空编码，属于医学/生物学领域，与推荐系统、搜索或广告完全无关。动态课程学习虽然是机器学习技术，但在此特定医学应用背景下，没有明显的推荐/搜索/广告应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 06:10:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15151v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15151v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                    High-dimensional neuroimaging analyses for clinical diagnosis are often constrained by compromises in spatiotemporal fidelity and by the limited adaptability of large-scale, general-purpose models. To address these challenges, we introduce Dynamic Curriculum Learning for Spatiotemporal Encoding (DCL-SE), an end-to-end framework centered on data-driven spatiotemporal encoding (DaSE). We leverage Approximate Rank Pooling (ARP) to efficiently encode three-dimensional volumetric brain data into information-rich, two-dimensional dynamic representations, and then employ a dynamic curriculum learning strategy, guided by a Dynamic Group Mechanism (DGM), to progressively train the decoder, refining feature extraction from global anatomical structures to fine pathological details. Evaluated across six publicly available datasets, including Alzheimer's disease and brain tumor classification, cerebral artery segmentation, and brain age prediction, DCL-SE consistently outperforms existing methods in accuracy, robustness, and interpretability. These findings underscore the critical importance of compact, task-specific architectures in the era of large-scale pretrained networks.
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            <a href="https://www.alphaxiv.org/abs/2511.15132v1" target="_blank" rel="noopener noreferrer">
                WaveFuse-AL：用于医学图像的循环式性能自适应多策略主动学习
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            WaveFuse-AL: Cyclical and Performance-Adaptive Multi-Strategy Active Learning for Medical Images
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nishchala Thakur, Swati Kochhar, Deepti R. Bathula, Sukrit Gupta
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学图像领域的主动学习方法，属于医疗领域特定应用，与推荐系统、搜索或广告无关。主动学习技术本身可能具有通用性，但论文明确限定于医学图像应用场景，且未提及任何与推荐系统、搜索或广告相关的潜在应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 05:23:23
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                <a href="https://arxiv.org/abs/2511.15132v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15132v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    Active learning reduces annotation costs in medical imaging by strategically selecting the most informative samples for labeling. However, individual acquisition strategies often exhibit inconsistent behavior across different stages of the active learning cycle. We propose Cyclical and Performance-Adaptive Multi-Strategy Active Learning (WaveFuse-AL), a novel framework that adaptively fuses multiple established acquisition strategies-BALD, BADGE, Entropy, and CoreSet throughout the learning process. WaveFuse-AL integrates cyclical (sinusoidal) temporal priors with performance-driven adaptation to dynamically adjust strategy importance over time. We evaluate WaveFuse-AL on three medical imaging benchmarks: APTOS-2019 (multi-class classification), RSNA Pneumonia Detection (binary classification), and ISIC-2018 (skin lesion segmentation). Experimental results demonstrate that WaveFuse-AL consistently outperforms both single-strategy and alternating-strategy baselines, achieving statistically significant performance improvements (on ten out of twelve metric measurements) while maximizing the utility of limited annotation budgets.
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            <a href="https://www.alphaxiv.org/abs/2511.15117v1" target="_blank" rel="noopener noreferrer">
                面向老年人家庭监护的社交劝导与危险预警事件触发系统
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            An Event-triggered System for Social Persuasion and Danger Alert in Elder Home Monitoring
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jun-Yi Liu, Chung-Hao Chen, Ya-Chi Tsao, Ssu-Yao Wu, Yu-Ting Tsao, Lyn Chao-ling...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于老年人监护领域的特定应用系统，涉及社交劝导和危险预警功能。这属于医疗/健康监护的特定领域应用，与推荐系统、搜索、广告或相关LLM技术无直接关联，完全超出了我的关注范围。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 04:39:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15117v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15117v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.MM</span></div>
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                    In the study, the physical state and mental state of elders are both considered, and an event-triggered system has developed to detect events: watch dog, danger notice and photo link. By adopting GMM background modeling, the motion behavior of visitors and elders can be detected in the watch dog event and danger notice event respectively. Experiments set in home scenarios and 5 families participated in the experiments for detecting and recording three types of events from their life activities. In addition, the captured images were analyzed using SVM machine learning. For lack of technical experiences of elders, an intuitive operation as normal life activity was designed to create communication between elder and relatives via social media.
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            <a href="https://www.alphaxiv.org/abs/2511.15102v1" target="_blank" rel="noopener noreferrer">
                高斯混合：重新思考3D高斯泼溅中的Alpha混合
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            Gaussian Blending: Rethinking Alpha Blending in 3D Gaussian Splatting
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Junseo Koo, Jinseo Jeong, Gunhee Kim
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于计算机视觉中的3D重建技术，具体涉及3D高斯泼溅和渲染优化。该主题属于纯粹的3D视觉领域，与推荐系统、搜索或广告的核心技术焦点没有直接关联。论文内容不涉及任何可能应用于RecSys/Search/Ads的转换器架构、LLM技术或推荐算法。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 04:21:38
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                <a href="https://arxiv.org/abs/2511.15102v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15102v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    The recent introduction of 3D Gaussian Splatting (3DGS) has significantly advanced novel view synthesis. Several studies have further improved the rendering quality of 3DGS, yet they still exhibit noticeable visual discrepancies when synthesizing views at sampling rates unseen during training. Specifically, they suffer from (i) erosion-induced blurring artifacts when zooming in and (ii) dilation-induced staircase artifacts when zooming out. We speculate that these artifacts arise from the fundamental limitation of the alpha blending adopted in 3DGS methods. Instead of the conventional alpha blending that computes alpha and transmittance as scalar quantities over a pixel, we propose to replace it with our novel Gaussian Blending that treats alpha and transmittance as spatially varying distributions. Thus, transmittances can be updated considering the spatial distribution of alpha values across the pixel area, allowing nearby background splats to contribute to the final rendering. Our Gaussian Blending maintains real-time rendering speed and requires no additional memory cost, while being easily integrated as a drop-in replacement into existing 3DGS-based or other NVS frameworks. Extensive experiments demonstrate that Gaussian Blending effectively captures fine details at various sampling rates unseen during training, consistently outperforming existing novel view synthesis models across both unseen and seen sampling rates.
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            <a href="https://www.alphaxiv.org/abs/2511.15085v1" target="_blank" rel="noopener noreferrer">
                TiCAL：基于典型性的一致性感知学习用于多模态情感识别
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            TiCAL:Typicality-Based Consistency-Aware Learning for Multimodal Emotion Recognition
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wen Yin, Siyu Zhan, Cencen Liu, Xin Hu, Guiduo Duan, Xiurui Xie, Yuan-Fang Li, T...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于多模态情感识别，属于情感计算和心理学应用领域，与推荐系统、搜索或广告的核心技术无关。虽然提到了多模态学习，但其应用场景（情感识别）和核心技术（典型性一致性学习）在推荐/搜索/广告领域没有直接相关性或潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 03:49:22
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                <a href="https://arxiv.org/abs/2511.15085v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15085v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Multimodal Emotion Recognition (MER) aims to accurately identify human emotional states by integrating heterogeneous modalities such as visual, auditory, and textual data. Existing approaches predominantly rely on unified emotion labels to supervise model training, often overlooking a critical challenge: inter-modal emotion conflicts, wherein different modalities within the same sample may express divergent emotional tendencies. In this work, we address this overlooked issue by proposing a novel framework, Typicality-based Consistent-aware Multimodal Emotion Recognition (TiCAL), inspired by the stage-wise nature of human emotion perception. TiCAL dynamically assesses the consistency of each training sample by leveraging pseudo unimodal emotion labels alongside a typicality estimation. To further enhance emotion representation, we embed features in a hyperbolic space, enabling the capture of fine-grained distinctions among emotional categories. By incorporating consistency estimates into the learning process, our method improves model performance, particularly on samples exhibiting high modality inconsistency. Extensive experiments on benchmark datasets, e.g, CMU-MOSEI and MER2023, validate the effectiveness of TiCAL in mitigating inter-modal emotional conflicts and enhancing overall recognition accuracy, e.g., with about 2.6% improvements over the state-of-the-art DMD.
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            <a href="https://www.alphaxiv.org/abs/2511.15077v1" target="_blank" rel="noopener noreferrer">
                MambaTrack3D：基于状态空间模型的激光雷达目标跟踪框架，适用于高时间变化场景
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            MambaTrack3D: A State Space Model Framework for LiDAR-Based Object Tracking under High Temporal Variation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shengjing Tian, Yinan Han, Xiantong Zhao, Xuehu Liu, Qi Lang
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于激光雷达3D目标跟踪，属于纯粹的计算机视觉和自动驾驶领域。虽然提到了状态空间模型，但该技术在此上下文中的具体实现与推荐系统、搜索或广告的核心技术栈没有明显关联。论文的应用场景（自动驾驶中的目标跟踪）与我的关注领域相距甚远。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 03:37:56
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                <a href="https://arxiv.org/abs/2511.15077v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15077v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Dynamic outdoor environments with high temporal variation (HTV) pose significant challenges for 3D single object tracking in LiDAR point clouds. Existing memory-based trackers often suffer from quadratic computational complexity, temporal redundancy, and insufficient exploitation of geometric priors. To address these issues, we propose MambaTrack3D, a novel HTV-oriented tracking framework built upon the state space model Mamba. Specifically, we design a Mamba-based Inter-frame Propagation (MIP) module that replaces conventional single-frame feature extraction with efficient inter-frame propagation, achieving near-linear complexity while explicitly modeling spatial relations across historical frames. Furthermore, a Grouped Feature Enhancement Module (GFEM) is introduced to separate foreground and background semantics at the channel level, thereby mitigating temporal redundancy in the memory bank. Extensive experiments on KITTI-HTV and nuScenes-HTV benchmarks demonstrate that MambaTrack3D consistently outperforms both HTV-oriented and normal-scenario trackers, achieving improvements of up to 6.5 success and 9.5 precision over HVTrack under moderate temporal gaps. On the standard KITTI dataset, MambaTrack3D remains highly competitive with state-of-the-art normal-scenario trackers, confirming its strong generalization ability. Overall, MambaTrack3D achieves a superior accuracy-efficiency trade-off, delivering robust performance across both specialized HTV and conventional tracking scenarios.
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            <a href="https://www.alphaxiv.org/abs/2511.15067v1" target="_blank" rel="noopener noreferrer">
                深度病理学习定义结直肠癌的预后亚型与分子驱动因素
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            Deep Pathomic Learning Defines Prognostic Subtypes and Molecular Drivers in Colorectal Cancer
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zisong Wang, Xuanyu Wang, Hang Chen, Haizhou Wang, Yuxin Chen, Yihang Xu, Yunhe ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的癌症病理分析，属于明确的无关主题范畴。其内容涉及结直肠癌的预后亚型和分子驱动因素研究，与推荐系统、搜索、广告或相关使能技术没有任何关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 03:19:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15067v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15067v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span><span class="category-tag">q-bio.GN</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Precise prognostic stratification of colorectal cancer (CRC) remains a major clinical challenge due to its high heterogeneity. The conventional TNM staging system is inadequate for personalized medicine. We aimed to develop and validate a novel multiple instance learning model TDAM-CRC using histopathological whole-slide images for accurate prognostic prediction and to uncover its underlying molecular mechanisms. We trained the model on the TCGA discovery cohort (n=581), validated it in an independent external cohort (n=1031), and further we integrated multi-omics data to improve model interpretability and identify novel prognostic biomarkers. The results demonstrated that the TDAM-CRC achieved robust risk stratification in both cohorts. Its predictive performance significantly outperformed the conventional clinical staging system and multiple state-of-the-art models. The TDAM-CRC risk score was confirmed as an independent prognostic factor in multivariable analysis. Multi-omics analysis revealed that the high-risk subtype is closely associated with metabolic reprogramming and an immunosuppressive tumor microenvironment. Through interaction network analysis, we identified and validated Mitochondrial Ribosomal Protein L37 (MRPL37) as a key hub gene linking deep pathomic features to clinical prognosis. We found that high expression of MRPL37, driven by promoter hypomethylation, serves as an independent biomarker of favorable prognosis. Finally, we constructed a nomogram incorporating the TDAM-CRC risk score and clinical factors to provide a precise and interpretable clinical decision-making tool for CRC patients. Our AI-driven pathological model TDAM-CRC provides a robust tool for improved CRC risk stratification, reveals new molecular targets, and facilitates personalized clinical decision-making.
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            <a href="https://www.alphaxiv.org/abs/2511.15066v1" target="_blank" rel="noopener noreferrer">
                BokehFlow：通过流匹配实现无需深度的可控景深渲染
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
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            BokehFlow: Depth-Free Controllable Bokeh Rendering via Flow Matching
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yachuan Huang, Xianrui Luo, Qiwen Wang, Liao Shen, Jiaqi Li, Huiqiang Sun, Zihao...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的景深渲染技术，属于纯粹的视觉处理领域。虽然流匹配是生成模型的一种方法，但该工作没有展示与推荐系统、搜索或广告的明显关联，也不涉及Transformer架构改进或LLM技术应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 03:18:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15066v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15066v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Bokeh rendering simulates the shallow depth-of-field effect in photography, enhancing visual aesthetics and guiding viewer attention to regions of interest. Although recent approaches perform well, rendering controllable bokeh without additional depth inputs remains a significant challenge. Existing classical and neural controllable methods rely on accurate depth maps, while generative approaches often struggle with limited controllability and efficiency. In this paper, we propose BokehFlow, a depth-free framework for controllable bokeh rendering based on flow matching. BokehFlow directly synthesizes photorealistic bokeh effects from all-in-focus images, eliminating the need for depth inputs. It employs a cross-attention mechanism to enable semantic control over both focus regions and blur intensity via text prompts. To support training and evaluation, we collect and synthesize four datasets. Extensive experiments demonstrate that BokehFlow achieves visually compelling bokeh effects and offers precise control, outperforming existing depth-dependent and generative methods in both rendering quality and efficiency.
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            <a href="https://www.alphaxiv.org/abs/2511.15065v1" target="_blank" rel="noopener noreferrer">
                基于视频的推理：通过迷宫求解任务对视频模型推理能力的首次评估
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            Reasoning via Video: The First Evaluation of Video Models' Reasoning Abilities through Maze-Solving Tasks
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Cheng Yang, Haiyuan Wan, Yiran Peng, Xin Cheng, Zhaoyang Yu, Jiayi Zhang, Junchi...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视频模型的推理能力评估，属于纯粹的视觉推理研究领域。虽然涉及模型能力评估，但主要针对视频模态的迷宫求解任务，与推荐系统、搜索或广告的核心技术发展没有直接关联，也不涉及Transformer架构改进或LLM技术在相关领域的应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 03:18:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15065v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15065v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Video Models have achieved remarkable success in high-fidelity video generation with coherent motion dynamics. Analogous to the development from text generation to text-based reasoning in language modeling, the development of video models motivates us to ask: Can video models reason via video generation? Compared with the discrete text corpus, video grounds reasoning in explicit spatial layouts and temporal continuity, which serves as an ideal substrate for spatial reasoning. In this work, we explore the reasoning via video paradigm and introduce VR-Bench -- a comprehensive benchmark designed to systematically evaluate video models' reasoning capabilities. Grounded in maze-solving tasks that inherently require spatial planning and multi-step reasoning, VR-Bench contains 7,920 procedurally generated videos across five maze types and diverse visual styles. Our empirical analysis demonstrates that SFT can efficiently elicit the reasoning ability of video model. Video models exhibit stronger spatial perception during reasoning, outperforming leading VLMs and generalizing well across diverse scenarios, tasks, and levels of complexity. We further discover a test-time scaling effect, where diverse sampling during inference improves reasoning reliability by 10--20%. These findings highlight the unique potential and scalability of reasoning via video for spatial reasoning tasks.
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            <a href="https://www.alphaxiv.org/abs/2511.15060v1" target="_blank" rel="noopener noreferrer">
                基于ADMM的变换L1（TL1）正则化图像去噪
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            Image Denoising Using Transformed L1 (TL1) Regularization via ADMM
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nabiha Choudhury, Jianqing Jia, Yifei Lou
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于图像处理领域的去噪技术，使用变换L1正则化和ADMM优化方法。这与我的关注点完全无关，因为图像去噪在推荐系统、搜索或广告中没有直接应用潜力，并且不涉及LLM、Transformer架构或异构数据建模。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 03:06:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15060v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15060v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">eess.IV</span><span class="category-tag">cs.CV</span><span class="category-tag">math.OC</span></div>
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                    Total variation (TV) regularization is a classical tool for image denoising, but its convex $\ell_1$ formulation often leads to staircase artifacts and loss of contrast. To address these issues, we introduce the Transformed $\ell_1$ (TL1) regularizer applied to image gradients. In particular, we develop a TL1-regularized denoising model and solve it using the Alternating Direction Method of Multipliers (ADMM), featuring a closed-form TL1 proximal operator and an FFT-based image update under periodic boundary conditions. Experimental results demonstrate that our approach achieves superior denoising performance, effectively suppressing noise while preserving edges and enhancing image contrast.
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            <a href="https://www.alphaxiv.org/abs/2511.15057v1" target="_blank" rel="noopener noreferrer">
                ProPL：通过提示引导的伪标签实现通用半监督超声图像分割
            </a>
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            ProPL: Universal Semi-Supervised Ultrasound Image Segmentation via Prompt-Guided Pseudo-Labeling
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yaxiong Chen, Qicong Wang, Chunlei Li, Jingliang Hu, Yilei Shi, Shengwu Xiong, X...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学超声图像分割，属于医疗领域的计算机视觉应用。虽然涉及半监督学习和伪标签技术，但这些方法与推荐系统、搜索或广告没有直接关联。论文的技术内容纯粹针对医疗图像处理，不涉及任何推荐、搜索或广告领域的潜在应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 03:01:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15057v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15057v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Existing approaches for the problem of ultrasound image segmentation, whether supervised or semi-supervised, are typically specialized for specific anatomical structures or tasks, limiting their practical utility in clinical settings. In this paper, we pioneer the task of universal semi-supervised ultrasound image segmentation and propose ProPL, a framework that can handle multiple organs and segmentation tasks while leveraging both labeled and unlabeled data. At its core, ProPL employs a shared vision encoder coupled with prompt-guided dual decoders, enabling flexible task adaptation through a prompting-upon-decoding mechanism and reliable self-training via an uncertainty-driven pseudo-label calibration (UPLC) module. To facilitate research in this direction, we introduce a comprehensive ultrasound dataset spanning 5 organs and 8 segmentation tasks. Extensive experiments demonstrate that ProPL outperforms state-of-the-art methods across various metrics, establishing a new benchmark for universal ultrasound image segmentation.
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            <a href="https://www.alphaxiv.org/abs/2511.15054v1" target="_blank" rel="noopener noreferrer">
                CellGenNet：一种用于癌症组织中稳健细胞分割的知识蒸馏框架
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            CellGenNet: A Knowledge-Distilled Framework for Robust Cell Segmentation in Cancer Tissues
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Srijan Ray, Bikesh K. Nirala, Jason T. Yustein, Sundaresh Ram
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学图像分析中的细胞分割任务，属于生物医学领域的具体应用。虽然涉及知识蒸馏技术，但其应用场景（癌症组织细胞分割）与推荐系统、搜索或广告领域没有任何直接关联，也不具备在这些领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 02:54:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15054v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15054v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Accurate nuclei segmentation in microscopy whole slide images (WSIs) remains challenging due to variability in staining, imaging conditions, and tissue morphology. We propose CellGenNet, a knowledge distillation framework for robust cross-tissue cell segmentation under limited supervision. CellGenNet adopts a student-teacher architecture, where a capacity teacher is trained on sparse annotations and generates soft pseudo-labels for unlabeled regions. The student is optimized using a joint objective that integrates ground-truth labels, teacher-derived probabilistic targets, and a hybrid loss function combining binary cross-entropy and Tversky loss, enabling asymmetric penalties to mitigate class imbalance and better preserve minority nuclear structures. Consistency regularization and layerwise dropout further stabilize feature representations and promote reliable feature transfer. Experiments across diverse cancer tissue WSIs show that CellGenNet improves segmentation accuracy and generalization over supervised and semi-supervised baselines, supporting scalable and reproducible histopathology analysis.
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            <a href="https://www.alphaxiv.org/abs/2511.15052v1" target="_blank" rel="noopener noreferrer">
                基于退化低秩与残差融合方法的带图像间变异性的高光谱超分辨率
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            Hyperspectral Super-Resolution with Inter-Image Variability via Degradation-based Low-Rank and Residual Fusion Method
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yue Wen, Kunjing Yang, Minru Bai
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于高光谱图像超分辨率，这是一个纯粹的计算机视觉任务，与推荐系统、搜索或广告没有直接关联。论文标题中提到的低秩和残差融合方法属于图像处理技术，在推荐、搜索或广告领域没有明显的应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 02:45:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15052v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15052v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The fusion of hyperspectral image (HSI) with multispectral image (MSI) provides an effective way to enhance the spatial resolution of HSI. However, due to different acquisition conditions, there may exist spectral variability and spatially localized changes between HSI and MSI, referred to as inter-image variability, which can significantly affect the fusion performance. Existing methods typically handle inter-image variability by applying direct transformations to the images themselves, which can exacerbate the ill-posedness of the fusion model. To address this challenge, we propose a Degradation-based Low-Rank and Residual Fusion (DLRRF) model. First, we model the spectral variability as change in the spectral degradation operator. Second, to recover the lost spatial details caused by spatially localized changes, we decompose the target HSI into low rank and residual components, where the latter is used to capture the lost details. By exploiting the spectral correlation within the images, we perform dimensionality reduction on both components. Additionally, we introduce an implicit regularizer to utilize the spatial prior information from the images. The proposed DLRRF model is solved using the Proximal Alternating Optimization (PAO) algorithm within a Plug-and-Play (PnP) framework, where the subproblem regarding implicit regularizer is addressed by an external denoiser. We further provide a comprehensive convergence analysis of the algorithm. Finally, extensive numerical experiments demonstrate that DLRRF achieves superior performance in fusing HSI and MSI with inter-image variability.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.15046v1" target="_blank" rel="noopener noreferrer">
                UniHOI：通过统一标记空间实现统一的人-物交互理解
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>1/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            UniHOI: Unified Human-Object Interaction Understanding via Unified Token Space
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Panqi Yang, Haodong Jing, Nanning Zheng, Yongqiang Ma
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的人-物交互理解，属于纯粹的视觉任务。虽然标题提到'统一'概念，但内容与推荐系统、搜索或广告的核心技术领域没有直接关联，也不涉及LLM技术或Transformer架构的改进。这种视觉交互理解技术难以转化为RecSys/Search/Ads领域的实际应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 02:37:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15046v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15046v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In the field of human-object interaction (HOI), detection and generation are two dual tasks that have traditionally been addressed separately, hindering the development of comprehensive interaction understanding. To address this, we propose UniHOI, which jointly models HOI detection and generation via a unified token space, thereby effectively promoting knowledge sharing and enhancing generalization. Specifically, we introduce a symmetric interaction-aware attention module and a unified semi-supervised learning paradigm, enabling effective bidirectional mapping between images and interaction semantics even under limited annotations. Extensive experiments demonstrate that UniHOI achieves state-of-the-art performance in both HOI detection and generation. Specifically, UniHOI improves accuracy by 4.9% on long-tailed HOI detection and boosts interaction metrics by 42.0% on open-vocabulary generation tasks.
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            <a href="https://www.alphaxiv.org/abs/2511.15029v1" target="_blank" rel="noopener noreferrer">
                人类几何与数值概念发展的计算机视觉建模
            </a>
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            Computer Vision Modeling of the Development of Geometric and Numerical Concepts in Humans
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zekun Wang, Sashank Varma
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于计算机视觉在认知发展研究中的应用，特别是几何和数值概念的人类发展建模，这属于认知科学和计算机视觉的交叉领域。论文内容与推荐系统、搜索或广告的核心技术进展无关，也不涉及LLM技术、Transformer架构改进或异构数据统一建模等当前关注领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 01:50:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15029v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15029v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Mathematical thinking is a fundamental aspect of human cognition. Cognitive scientists have investigated the mechanisms that underlie our ability to thinking geometrically and numerically, to take two prominent examples, and developmental scientists have documented the trajectories of these abilities over the lifespan. Prior research has shown that computer vision (CV) models trained on the unrelated task of image classification nevertheless learn latent representations of geometric and numerical concepts similar to those of adults. Building on this demonstrated cognitive alignment, the current study investigates whether CV models also show developmental alignment: whether their performance improvements across training to match the developmental progressions observed in children. In a detailed case study of the ResNet-50 model, we show that this is the case. For the case of geometry and topology, we find developmental alignment for some classes of concepts (Euclidean Geometry, Geometrical Figures, Metric Properties, Topology) but not others (Chiral Figures, Geometric Transformations, Symmetrical Figures). For the case of number, we find developmental alignment in the emergence of a human-like ``mental number line'' representation with experience. These findings show the promise of computer vision models for understanding the development of mathematical understanding in humans. They point the way to future research exploring additional model architectures and building larger benchmarks.
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            <a href="https://www.alphaxiv.org/abs/2511.15022v1" target="_blank" rel="noopener noreferrer">
                用于计算机生成全息术的复值二维高斯表示
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        <div class="mb-2 text-base text-gray-700">
            Complex-Valued 2D Gaussian Representation for Computer-Generated Holography
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yicheng Zhan, Xiangjun Gao, Long Quan, Kaan Akşit
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机生成全息术，这是一个纯粹的计算机图形学应用领域，与推荐系统、搜索或广告没有直接关联。论文标题中提到的复值二维高斯表示技术主要面向全息显示和光学计算，在推荐系统、搜索或广告领域没有明显的应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 01:41:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.15022v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.15022v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.GR</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We propose a new hologram representation based on structured complex-valued 2D Gaussian primitives, which replaces per-pixel information storage and reduces the parameter search space by up to 10:1. To enable end-to-end training, we develop a differentiable rasterizer for our representation, integrated with a GPU-optimized light propagation kernel in free space. Our extensive experiments show that our method achieves up to 2.5x lower VRAM usage and 50% faster optimization while producing higher-fidelity reconstructions than existing methods. We further introduce a conversion procedure that adapts our representation to practical hologram formats, including smooth and random phase-only holograms. Our experiments show that this procedure can effectively suppress noise artifacts observed in previous methods. By reducing the hologram parameter search space, our representation enables a more scalable hologram estimation in the next-generation computer-generated holography systems.
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            <a href="https://www.alphaxiv.org/abs/2511.14998v1" target="_blank" rel="noopener noreferrer">
                FinCriticalED：用于金融事实级别OCR评估的视觉基准
            </a>
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            FinCriticalED: A Visual Benchmark for Financial Fact-Level OCR Evaluation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yueru He, Xueqing Peng, Yupeng Cao, Yan Wang, Lingfei Qian, Haohang Li, Yi Han, ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于金融领域的OCR评估基准，属于计算机视觉中的文档分析范畴。虽然OCR技术在理论上可以用于处理搜索或推荐系统中的文本数据，但该论文明确限定在金融领域的事实级别评估，且没有提及任何与推荐系统、搜索或广告相关的应用场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-19 00:41:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14998v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14998v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We introduce FinCriticalED (Financial Critical Error Detection), a visual benchmark for evaluating OCR and vision language models on financial documents at the fact level. Financial documents contain visually dense and table heavy layouts where numerical and temporal information is tightly coupled with structure. In high stakes settings, small OCR mistakes such as sign inversion or shifted dates can lead to materially different interpretations, while traditional OCR metrics like ROUGE and edit distance capture only surface level text similarity. \ficriticaled provides 500 image-HTML pairs with expert annotated financial facts covering over seven hundred numerical and temporal facts. It introduces three key contributions. First, it establishes the first fact level evaluation benchmark for financial document understanding, shifting evaluation from lexical overlap to domain critical factual correctness. Second, all annotations are created and verified by financial experts with strict quality control over signs, magnitudes, and temporal expressions. Third, we develop an LLM-as-Judge evaluation pipeline that performs structured fact extraction and contextual verification for visually complex financial documents. We benchmark OCR systems, open source vision language models, and proprietary models on FinCriticalED. Results show that although the strongest proprietary models achieve the highest factual accuracy, substantial errors remain in visually intricate numerical and temporal contexts. Through quantitative evaluation and expert case studies, FinCriticalED provides a rigorous foundation for advancing visual factual precision in financial and other precision critical domains.
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                        displayCountElement.textContent = `显示 ${allPapers.length} 篇论文 (共 ${allPapers.length} 篇)`;
                    }
                    
                    // 更新按钮样式
                    this.className = 'px-3 py-1 bg-primary text-white rounded text-sm hover:bg-primary/90 transition-colors';
                    document.getElementById('show-selected').className = 'px-3 py-1 bg-gray-200 text-gray-700 rounded text-sm hover:bg-gray-300 transition-colors';
                    
                    // 重新显示展开/折叠按钮和分割线
                    const expandToggle = document.querySelector('.expand-toggle');
                    if (expandToggle) {
                        expandToggle.style.display = 'block';
                        expandToggle.textContent = '展开全部非精选论文';
                    }
                    
                    const papersDivider = document.querySelector('.papers-divider');
                    if (papersDivider) papersDivider.style.display = 'block';
                });
            }
        });
    </script>
    <script>
    
    // 初始化日历
    document.addEventListener('DOMContentLoaded', () => {
        try {
            console.log('Attempting to initialize calendar...');
            initCalendar();
        } catch (error) {
            console.error('Error initializing calendar:', error);
        }
    });
    
    // 日历初始化函数
    function initCalendar() {
        const toggleBtn = document.getElementById('date-picker-toggle');
        const datePicker = document.getElementById('date-picker');
        const calendarGrid = document.getElementById('calendar-grid');
        const prevMonthBtn = document.getElementById('prev-month');
        const nextMonthBtn = document.getElementById('next-month');
        const currentMonthEl = document.getElementById('current-month');
        const selectedDateText = document.getElementById('selected-date-text');
        
        // 当前显示的日期（从页面获取）
        const currentDateStr = document.getElementById('current-date').textContent.trim().replace(/^\d+年|月|日/g, '');
        const currentDate = new Date(currentDateStr);
        let displayYear = currentDate.getFullYear();
        let displayMonth = currentDate.getMonth();
        
        // 有论文数据的日期列表
        const availableDates = ["20251105","20251107","20251009","20251113","20251030","20251111","20251031","20251017","20251021","20251010","20251024","20251022","20251029","20251114","20251118","20251120","20251016","20251015","20251028","20251014","20251119","20251112","20251106","20251023"];
        
        // 尝试从localStorage恢复选择状态
        const savedDate = localStorage.getItem('selectedDate');
        const savedYear = localStorage.getItem('selectedYear');
        const savedMonth = localStorage.getItem('selectedMonth');
        
        // 确保页面加载时显示当前选中的日期
        // 修复持久化问题：确保每次加载都能正确恢复选中状态
        if (savedDate) {
            selectedDateText.textContent = savedDate;
            if (savedYear) displayYear = parseInt(savedYear);
            if (savedMonth) displayMonth = parseInt(savedMonth);
        } else {
            // 首次加载时，将当前页面日期保存到localStorage
            const currentPageDate = currentDateStr.replace(/\//g, '-');
            selectedDateText.textContent = currentPageDate;
            localStorage.setItem('selectedDate', currentPageDate);
            localStorage.setItem('selectedYear', currentDate.getFullYear().toString());
            localStorage.setItem('selectedMonth', currentDate.getMonth().toString());
        }
    
        // 切换日历显示状态
        toggleBtn.addEventListener('click', (e) => {
            e.stopPropagation();
            
            // 显式控制hidden类的添加和移除
            if (datePicker.classList.contains('hidden')) {
                // 显示日历 - 确保移除hidden类
                datePicker.classList.remove('hidden');
                renderCalendar();
            } else {
                // 隐藏日历
                datePicker.classList.add('hidden');
            }
        });
        
        // 点击其他区域关闭日历
        document.addEventListener('click', () => {
            if (!datePicker.classList.contains('hidden')) {
                datePicker.classList.add('hidden');
            }
        });
        
        // 阻止日历内部点击事件冒泡
        datePicker.addEventListener('click', (e) => {
            e.stopPropagation();
        });
        
        // 上月和下月按钮
        prevMonthBtn.addEventListener('click', () => {
            displayMonth--;
            if (displayMonth < 0) {
                displayMonth = 11;
                displayYear--;
            }
            renderCalendar();
        });
        
        nextMonthBtn.addEventListener('click', () => {
            displayMonth++;
            if (displayMonth > 11) {
                displayMonth = 0;
                displayYear++;
            }
            renderCalendar();
        });
        
        /**
         * 渲染日历
         */
        function renderCalendar() {
            // 清空日历网格
            calendarGrid.innerHTML = '';
            
            // 更新当前月份显示
            const monthNames = ['1月', '2月', '3月', '4月', '5月', '6月', '7月', '8月', '9月', '10月', '11月', '12月'];
            currentMonthEl.textContent = displayYear + '年' + monthNames[displayMonth];
            
            // 计算当前月份的第一天是星期几
            const firstDay = new Date(displayYear, displayMonth, 1);
            const firstDayOfWeek = firstDay.getDay();
            
            // 计算当前月份的天数
            const daysInMonth = new Date(displayYear, displayMonth + 1, 0).getDate();
            
            // 添加上月的占位天数
            for (let i = 0; i < firstDayOfWeek; i++) {
                const emptyDay = document.createElement('div');
                emptyDay.classList.add('py-1', 'text-gray-300');
                calendarGrid.appendChild(emptyDay);
            }
            
            // 获取当前日期（用于高亮显示）
            const today = new Date();
            today.setHours(0, 0, 0, 0);
            
            // 添加当前月份的天数
            for (let day = 1; day <= daysInMonth; day++) {
                const dayElement = document.createElement('div');
                const currentDateObj = new Date(displayYear, displayMonth, day);
                const dateStr = displayYear + String(displayMonth + 1).padStart(2, '0') + String(day).padStart(2, '0');
                const displayDateStr = displayYear + '-' + String(displayMonth + 1).padStart(2, '0') + '-' + String(day).padStart(2, '0');
                
                // 设置日期元素基本样式
                dayElement.textContent = day;
                
                // 检查该日期是否有论文数据
                const hasPapers = availableDates.includes(dateStr);
                
                if (hasPapers) {
                    // 有论文数据的日期样式
                    dayElement.classList.add('py-1', 'cursor-pointer', 'hover:bg-gray-100', 'rounded', 'bg-blue-50', 'font-medium');
                    
                    // 添加点击事件，跳转到对应日期的页面
                    dayElement.addEventListener('click', () => {
                        console.log('Date clicked:', displayDateStr);
                        selectedDateText.textContent = displayDateStr;
                        
                        // 保存选择状态到localStorage
                        localStorage.setItem('selectedDate', displayDateStr);
                        localStorage.setItem('selectedYear', displayYear.toString());
                        localStorage.setItem('selectedMonth', displayMonth.toString());
                        
                        datePicker.classList.add('hidden');
                        
                        // 构造目标URL并跳转
                        const targetUrl = 'arxiv_' + dateStr + '.html';
                        window.location.href = targetUrl;
                    });
                } else {
                    // 没有论文数据的日期样式（置灰不可点击）
                    dayElement.classList.add('py-1', 'text-gray-400', 'cursor-not-allowed');
                }
                
                // 高亮显示当天日期（覆盖之前的样式）
                if (currentDateObj.getTime() === today.getTime()) {
                    dayElement.classList.remove('bg-blue-50');
                    dayElement.classList.add('bg-primary', 'text-white', 'font-bold', 'shadow');
                    if (!hasPapers) {
                        // 当天没有论文时，仍然置灰但保持背景色
                        dayElement.classList.add('opacity-70');
                    }
                }
                
                // 高亮显示当前选中的日期
                if (displayDateStr === selectedDateText.textContent) {
                    dayElement.classList.add('font-bold', 'border-2', 'border-primary', 'rounded-lg', 'shadow-md');
                }
                
                // 增强有论文数据的日期样式，使其更明显
                if (hasPapers && currentDateObj.getTime() !== today.getTime()) {
                    dayElement.classList.add('bg-blue-100', 'hover:bg-blue-200', 'transition-colors', 'duration-200');
                }
                
                calendarGrid.appendChild(dayElement);
            }
        }
    }
    </script>
    </body>

</html>